BackgroundTuberculosis (TB) remains a significant public health challenge, particularly among vulnerable populations like children. This is especially true in Sub-Saharan Africa, where the burden of TB in children is substantial. Zambia ranks 21st among the top 30 high TB endemic countries globally. While studies have explored TB in adults in Zambia, the prevalence and associated factors in children are not well documented. This study aimed to determine the prevalence and sociodemographic, and clinical factors associated with active TB disease in hospitalized children under the age of 15 years at Livingstone University Teaching Hospital (LUTH), the largest referral center in Zambia’s Southern Province.MethodsThis retrospective cross-sectional study of 700 pediatric patients under 15 years old, utilized programmatic data from the Pediatrics Department at LUTH. A systematic sampling method was used to select participants from medical records. Data on demographics, medical conditions, anthropometric measurements, and blood tests were collected. Data analysis included descriptive statistics, chi-square tests, and multivariable logistic regression to identify factors associated with TB.ResultsThe median age was 24 months (interquartile range (IQR): 11, 60) and majority were male (56.7%, n = 397/700). Most participants were from urban areas (59.9%, n = 419/700), and 9.2% (n = 62/675) were living with HIV. Malnutrition and comorbidities were present in a significant portion of the participants (19.0% and 25.1%, respectively). The prevalence of active TB cases was 9.4% (n = 66/700) among hospitalized children. Persons living with HIV (Adjusted odds ratio (AOR) of 6.30; 95% confidence interval (CI) of 2.85, 13.89, p< 0.001), and those who were malnourished (AOR: 10.38, 95% CI: 4.78, 22.55, p< 0.001) had a significantly higher likelihood of developing active TB disease.ConclusionThis study revealed a prevalence 9.4% active TB among hospitalized children under 15 years at LUTH. HIV status and malnutrition emerged as significant factors associated with active TB disease. These findings emphasize the need for pediatric TB control strategies that prioritize addressing associated factors to effectively reduce the burden of tuberculosis in Zambian children.

The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% for the test data.

The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low-frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.

The features selected for this database come from the accelerometer and gyroscope 3-axial raw signals tAcc-XYZ and tGyro-XYZ. These time-domain signals (prefix 't' to denote time) were captured at a constant rate of 50 Hz. Then they were filtered using a median filter and a 3rd order low pass Butterworth filter with a corner frequency of 20 Hz to remove noise. Similarly, the acceleration signal was then separated into the body and gravity acceleration signals (tBodyAcc-XYZ and tGravityAcc-XYZ) using another low pass Butterworth filter with a corner frequency of 0.3 Hz.

Subsequently, the body l linear acceleration and angular velocity were derived in time to obtain Jerk signals (tBodyAccJerk-XYZ and tBodyGyroJerk-XYZ). Also the magnitude of these three-dimensional signals were calculated using the Euclidean norm (tBodyAccMag, tGravityAccMag, tBodyAccJerkMag, tBodyGyroMag, tBodyGyroJerkMag).

Finally a Fast Fourier Transform (FFT) was applied to some of these signals producing fBodyAcc-XYZ, fBodyAccJerk-XYZ, fBodyGyro-XYZ, fBodyAccJerkMag, fBodyGyroMag, fBodyGyroJerkMag. (Note the 'f' to indicate frequency domain signals).

These signals were used to estimate variables of the feature vector for each pattern: '-XYZ' is used to denote 3-axial signals in the X, Y, and Z directions.

tBodyAcc-XYZ tGravityAcc-XYZ tBodyAccJerk-XYZ tBodyGyro-XYZ tBodyGyroJerk-XYZ tBodyAccMag tGravityAccMag tBodyAccJerkMag tBodyGyroMag tBodyGyroJerkMag fBodyAcc-XYZ fBodyAccJerk-XYZ fBodyGyro-XYZ fBodyAccMag fBodyAccJerkMag fBodyGyroMag fBodyGyroJerkMag

The set of variables that were estimated from these signals are:

mean(): Mean value std(): Standard deviation mad(): Median absolute deviation max(): Largest value in array min(): Smallest value in array sma(): Signal magnitude area energy(): Energy measure. Sum of the squares divided by the number of values. iqr(): Interquartile range entropy(): Signal entropy arCoeff(): Autorregresion coefficients with Burg order equal to 4 correlation(): correlation coefficient between two signals maxInds(): index of the frequency component with the largest magnitude meanFreq(): Weighted average of the frequency components to obtain a mean frequency skewness(): skewness of the frequency domain signal kurtosis(): kurtosis of the frequency domain signal bandsEnergy(): Energy of a frequency interval within the 64 bins of the FFT of each window. angle(): Angle between two vectors.

Additional vectors are obtained by averaging the signals in a signal window sample. These are used on the angle() variable:

gravityMean tBodyAccMean tBodyAccJerkMean tBodyGyroMean tBodyGyroJerkMean

This data set consists of the following columns:

1 tBodyAcc-mean()-X 2 tBodyAcc-mean()-Y 3 tBodyAcc-mean()-Z 4 tBodyAcc-std()-X 5 tBodyAcc-std()-Y 6 tBodyAcc-std()-Z 7 tBodyAcc-mad()-X 8 tBodyAcc-mad()-Y 9 tBodyAcc-mad()-Z 10 tBodyAcc-max()-X 11 tBodyAcc-max()-Y 12 tBodyAcc-max()-Z 13 tBodyAcc-min()-X 14 tBodyAcc-min()-Y 15 tBodyAcc-min()-Z 16 tBodyAcc-sma() 17 tBodyAcc-energy()-X 18 tBodyAcc-energy()-Y 19 tBodyAcc-energy()-Z 20 tBodyAcc-iqr()-X 21 tBodyAcc-iqr()-Y 22 tBodyAcc-iqr()-Z 23 tBodyAcc-entropy()-X 24 tBodyAcc-entropy()-Y 25 tBodyAcc-entropy()-Z 26 tBodyAcc-arCoeff()-X,1 27 tBodyAcc-arCoeff()-X,2 28 tBodyAcc-arCoeff()-X,3 29 tBodyAcc-arCoeff()-X,4 30 tBodyAcc-arCoeff()-Y,1 31 tBodyAcc-arCoeff()-Y,2 32 tBodyAcc-arCoeff()-Y,3 33 tBodyAcc-arCoeff()-Y,4 34 tBodyAcc-arCoeff()-Z,1 35 tBodyAcc-arCoeff()-Z,2 36 tBodyAcc-arCoeff()-Z,3 37 tBodyAcc-arCoeff()-Z,4 38 tBodyAcc-correlation()-X,Y 39 tBodyAcc-correlation()-X,Z 40 tBodyAcc-correlation()-Y,Z 41 tGravityAcc-mean()-X 42 tGravit...

ABSTRACT Objective: To analyze computed tomography scans of paranasal sinuses of a series of patients with coronavirus disease 2019, and correlate the findings with the disease. Methods: Computed tomography scans of 95 adult patients who underwent a polymerase chain reaction test for severe acute respiratory syndrome coronavirus 2 were analyzed. Clinical data were obtained from patients’ records and telephone calls. Paranasal sinus opacification was graded and compared according to severe acute respiratory syndrome coronavirus 2 positivity. Results: Of the patients 28 (29.5%) tested positive for severe acute respiratory syndrome coronavirus 2 (median age 52 [range 26-95] years) and 67 were negative (median age 50 [range 18-95] years). Mucosal thickening was present in 97.4% of maxillary sinuses, 80% of anterior ethmoid air cells, 75.3% of posterior ethmoid air cells, 74.7% of frontal sinuses, and 66.3% of sphenoid sinuses. Minimal or mild mucosal thickening (score 1)and normally aerated sinuses (score 0) corresponded to 71.4% and 21.3% of all paranasal sinuses, respectively. The mean score of each paranasal sinus among severe acute respiratory syndrome coronavirus 2 positive and negative patients was 0.85±0.27 and 0.87±0.38, respectively (p=0.74). Median paranasal sinus opacification score among severe acute respiratory syndrome coronavirus 2 positive patients was 9 (interquartile range 8-10) compared to 9 (interquartile range 5-10) in negative patients (p=0.89). There was no difference in mean score adjusted for age and sex. Nasal congestion was more frequent in severe acute respiratory syndrome coronavirus 2 positive than negative patients (p=0.05). Conclusion: Severe acute respiratory syndrome coronavirus 2 infection was associated with patient recall of nasal congestion, but showed no correlation with opacification of paranasal sinuses.

The U.S. Climate Reference Network (USCRN) was designed to monitor the climate of the United States using research quality instrumentation located within representative pristine environments. This Standardized Soil Moisture (SSM) and Soil Moisture Climatology (SMC) product set is derived using the soil moisture observations from the USCRN. The hourly soil moisture anomaly (SMANOM) is derived by subtracting the MEDIAN from the soil moisture volumetric water content (SMVWC) and dividing the difference by the interquartile range (IQR = 75th percentile - 25th percentile) for that hour: SMANOM = (SMVWC - MEDIAN) / (IQR). The soil moisture percentile (SMPERC) is derived by taking all the values that were used to create the empirical cumulative distribution function (ECDF) that yielded the hourly MEDIAN and adding the current observation to the set, recalculating the ECDF, and determining the percentile value of the current observation. Finally, the soil temperature for the individual layers is provided for the dataset user convenience. The SMC files contain the MEAN, MEDIAN, IQR, and decimal fraction of available data that are valid for each hour of the year at 5, 10, 20, 50, and 100 cm depth soil layers as well as for a top soil layer (TOP) and column soil layer (COLUMN). The TOP layer consists of an average of the 5 and 10 cm depths, while the COLUMN layer includes all available depths at a location, either two layers or five layers depending on soil depth. The SSM files contain the mean VWC, SMANOM, SMPERC, and TEMPERATURE for each of the depth layers described above. File names are structured as CRNSSM0101-STATIONNAME.csv and CRNSMC0101-STATIONNAME.csv. SSM stands for Standardized Soil Moisture and SCM represent Soil Moisture Climatology. The first two digits of the trailing integer indicate major version and the second two digits minor version of the product.

Characteristics of the included medications.

A live version of the data record, which will be kept up-to-date with new estimates, can be downloaded from the Humanitarian Data Exchange: https://data.humdata.org/dataset/covid-19-mobility-italy.

If you find the data helpful or you use the data for your research, please cite our work:

Pepe, E., Bajardi, P., Gauvin, L., Privitera, F., Lake, B., Cattuto, C., & Tizzoni, M. (2020). COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Scientific Data 7, 230 (2020).

The data record is structured into 4 comma-separated value (CSV) files, as follows:

id_provinces_IT.csv. Table of the administrative codes of the 107 Italian provinces. The fields of the table are:

COD_PROV is an integer field that is used to identify a province in all other data records;

SIGLA is a two-letters code that identifies the province according to the ISO_3166-2 standard (https://en.wikipedia.org/wiki/ISO_3166-2:IT);

DEN_PCM is the full name of the province.

OD_Matrix_daily_flows_norm_full_2020_01_18_2020_04_17.csv. The file contains the daily fraction of users’ moving between Italian provinces. Each line corresponds to an entry of matrix (i, j). The fields of the table are:

p1: COD_PROV of origin,

p2: COD_PROV of destination,

day: in the format yyyy-mm-dd.

median_q1_q3_rog_2020_01_18_2020_04_17.csv. The file contains median and interquartile range (IQR) of users’ radius of gyration in a province by week. Each entry of the table fields of the table are:

COD_PROV of the province;

SIGLA of the province;

DEN_PCM of the province;

week: median value of the radius of gyration on week week, with week in the format dd/mm-DD/MM where dd/mm and DD/MM are the first and the last day of the week, respectively.

week Q1 first quartile (Q1) of the distribution of the radius of gyration on week week,

week Q3 third quartile (Q3) of the distribution of the radius of gyration on week week,

average_network_degree_2020_01_18_2020_04_17.csv. The file contains daily time-series of the average degree 〈k〉 of the proximity network. Each entry of the table is a value of 〈k〉 on a given day. The fields of the table are:

COD_PROV of the province;

SIGLA of the province;

DEN_PCM of the province;

day in the format yyyy-mm-dd.

ESRI shapefiles of the Italian provinces updated to the most recent definition are available from the website of the Italian National Office of Statistics (ISTAT): https://www.istat.it/it/archivio/222527.

📊 Walmart Stock Price Dataset & Exploratory Data Analysis (EDA)

🏢 About Walmart

Walmart Inc. is a multinational retail corporation that operates a chain of hypermarkets, discount department stores, and grocery stores. It is one of the world's largest companies by revenue and a key player in the retail sector. Walmart's stock is actively traded on major stock exchanges, making it an interesting subject for financial analysis.

📌 Dataset Overview

This dataset contains historical stock price data for Walmart, sourced directly from Yahoo Finance using the yfinance Python API. The data covers daily stock prices and includes multiple key financial indicators.

📊 Features Included in the Dataset

• Date 📅 – The trading day recorded.
• Open Price 🟢 – Price at market open.
• High Price 🔼 – Highest price of the day.
• Low Price 🔽 – Lowest price of the day.
• Close Price 🔴 – Price at market close.
• Adjusted Close Price 📉 – Closing price adjusted for splits & dividends.
• Trading Volume 📈 – Total shares traded.
• Dividends 💰 – Cash payments to shareholders.
• Stock Splits 🔄 – Records stock split events.

🔍 Exploratory Data Analysis (EDA) Steps

This notebook performs an extensive EDA to uncover insights into Walmart's stock price trends, volatility, and overall behavior in the stock market. The following analysis steps are included:

1️⃣ Data Preprocessing & Cleaning

• Load data using Pandas
• Handle missing values (if any)
• Check data types and format them properly
• Convert date column into a datetime format

2️⃣ Descriptive Statistics & Summary

• Calculate key statistical measures like mean, median, standard deviation, and interquartile range (IQR)
• Identify stock price trends over time
• Check data distribution and skewness

3️⃣ Data Visualizations

• 📉 Line Plot – Analyze trends in closing prices over time.
• 📦 Box Plot – Detect potential outliers in stock prices.
• 📊 Histogram – Understand the distribution of closing prices.
• 📈 Moving Averages – Use short-term and long-term moving averages to observe stock trends.
• 🔥 Correlation Heatmap – Find relationships between stock market indicators.

4️⃣ Time Series Analysis

• Identify trends and seasonality in the stock price data.
• Calculate daily, weekly, and monthly returns.
• Use rolling windows to analyze moving averages and volatility.

5️⃣ Insights & Conclusions

• How volatile is Walmart’s stock over the given period?
• Does the stock exhibit strong uptrends or downtrends?
• Are there any strong correlations between features?
• What insights can be drawn for investors and traders?

🚀 Use Cases & Applications

This dataset and analysis can be useful for: - 📡 Stock Market Analysis – Evaluating Walmart’s stock price trends and volatility. - 🏦 Investment Research – Assisting traders and investors in making informed decisions. - 🎓 Educational Purposes – Teaching data science and financial analysis using real-world stock data. - 📊 Algorithmic Trading – Developing trading strategies based on historical stock price trends.

📥 Download the dataset and explore Walmart’s stock performance today! 🚀

Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.

This table contains a source catalog based on 90-cm (324-MHz) Very Large Array (VLA) imaging of the COSMOS field, comprising a circular area of 3.14 square degrees centered on 10^h 00^m 28.6^s, 02^o 12' 21" (J2000.0 RA and Dec). The image from the merger of 3 nights of observations using all 27 VLA antennas had an effective total integration time of ~ 12 hours, an 8.0 arcsecond x 6.0 arcsecond angular resolution, and an average rms of 0.5 mJy beam^-1. The extracted catalog contains 182 sources (down to 5.5 sigma), 30 of which are multi-component sources. Using Monte Carlo artificial source simulations, the authors derive the completeness of the catalog, and show that their 90-cm source counts agree very well with those from previous studies. In their paper, the authors use X-ray, NUV-NIR and radio COSMOS data to investigate the population mix of this 90-cm radio sample, and find that the sample is dominated by active galactic nuclei. The average 90-20 cm spectral index (S_nu~ nu^alpha, where S_nu is the flux density at frequency nu and alpha the spectral index) of the 90-cm selected sources is -0.70, with an interquartile range from -0.90 to -0.53. Only a few ultra-steep-spectrum sources are present in this sample, consistent with results in the literature for similar fields. These data do not show clear steepening of the spectral index with redshift. Nevertheless, this sample suggests that sources with spectral indices steeper than -1 all lie at z >~ 1, in agreement with the idea that ultra-steep-spectrum radio sources may trace intermediate-redshift galaxies (z >~ 1). Using both the signal and rms maps (see Figs. 1 and 2 in the reference paper) as input data, the authors ran the AIPS task SAD to obtain a catalog of candidate components above a given local signal-to-noise ratio (S/N) threshold. The task SAD was run four times with search S/N levels of 10, 8, 6 and 5, using the resulting residual image each time. They recovered all the radio components with a local S/N > 5.00. Subsequently, all the selected components were visually inspected, in order to check their reliability, especially for the components near strong side-lobes. After a careful analysis, a S/N threshold of 5.50 was adopted as the best compromise between a deep and a reliable catalog. The procedure yielded a total of 246 components with a local S/N > 5.50. More than one component, identified in the 90-cm map sometimes belongs to a single radio source (e.g. large radio galaxies consist of multiple components). Using the 90-cm COSMOS radio map, the authors combined the various components into single sources based on visual inspection. The final catalog (contained in this HEASARC table) lists 182 radio sources, 30 of which have been classified as multiple, i.e. they are better described by more than a single component. Moreover, in order to ensure a more precise classification, all sources identified as multi-component sources have been also double-checked using the 20-cm radio map. The authors found that all the 26 multiple 90-cm radio sources within the 20-cm map have 20-cm counterpart sources already classified as multiple. The authors have made use of the VLA-COSMOS Large and Deep Projects over 2 square degrees, reaching down to an rms of ~15 µJy beam¹ ^ at 1.4 GHz and 1.5 arcsec resolution (Schinnerer et al. 2007, ApJS, 172, 46: the VLACOSMOS table in the HEASARC database). The 90-cm COSMOS radio catalog has, however, been extracted from a larger region of 3.14 square degrees (see Fig. 1 and Section 3.1 of the reference paper). This implies that a certain number of 90-cm sources (48) lie outside the area of the 20-cm COSMOS map used to select the radio catalog. Thus, to identify the 20-cm counterparts of the 90-cm radio sources, the authors used the joint VLA-COSMOS catalog (Schinnerer et al. 2010, ApJS, 188, 384: the VLACOSMJSC table in the HEASARC database) for the 134 sources within the 20-cm VLA-COSMOS area and the VLA- FIRST survey (White et al. 1997, ApJ, 475, 479: the FIRST table in the HEASARC database) for the remaining 48 sources. The 90-cm sources were cross-matched with the 20-cm VLA-COSMOS sources using a search radius of 2.5 arcseconds, while the cross-match with the VLA-FIRST sources has been done using a search radius of 4 arcseconds in order to take into account the larger synthesized beam of the VLA-FIRST survey of ~5 arcseconds. Finally, all the 90 cm - 20 cm associations were visually inspected in order to ensure also the association of the multiple 90-cm radio sources for which the value of the search radius used during the cross-match could be too restrictive. In summary, out of the total of 182 sources in the 90-cm catalog, 168 have counterparts at 20 cm. This table was created by the HEASARC in October 2014 based on an electronic version of Table 1 from the reference paper which was obtained from the COSMOS web site at IRSA, specifically the file vla-cosmos_327_sources_published_version.tbl at http://irsa.ipac.caltech.edu/data/COSMOS/tables/vla/. This is a service provided by NASA HEASARC .

Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this issue, we developed three machine-learning models to estimate peak horizontal power for level, uphill, and downhill running using gait spatiotemporal parameters, accelerometer, and gyroscope signals extracted from foot-worn IMUs. The prediction was compared to reference horizontal power obtained during running on a treadmill with an embedded force plate. For each model, we trained an elastic net and a neural network and validated it with a dataset of 34 active adults across a range of speeds and slopes. For the uphill and level running, the concentric phase of the gait cycle was considered, and the neural network model led to the lowest error (median ± interquartile range) of 1.7% ± 12.5% and 3.2% ± 13.4%, respectively. The eccentric phase was considered relevant for downhill running, wherein the elastic net model provided the lowest error of 1.8% ± 14.1%. Results showed a similar performance across a range of different speed/slope running conditions. The findings highlighted the potential of using interpretable biomechanical features in machine learning models for the estimating horizontal power. The simplicity of the models makes them suitable for implementation on embedded systems with limited processing and energy storage capacity. The proposed method meets the requirements for applications needing accurate near real-time feedback and complements existing gait analysis algorithms based on foot-worn IMUs.

Median Pearson correlation coefficient (PCC) and interquartile range (IQR) for all 3 datasets from Studies 1,2, and 3. Each PCC is separately calculated for the sagittal and coronal plane, and for the original smoothed line markings of SPL and ISL and the smoothed line markings after applying a Procrustes transformation to the ISL.

The influence of anesthesia type and duration on the occurrence of pulmonary embolism (PE) after surgery remains controversial. This study investigates the association between anesthesia type and duration with postoperative PE. A retrospective cohort of adult patients undergoing surgery from May 2020 to August 2024 at large-scale general hospitals was analyzed. Multivariable logistic regression models were employed to adjust for potential confounders, and sensitivity analyses (using overlap weighting and array approach) were performed to validate the findings. A total of 178,052 patients were included in the analysis, of whom 91 developed PE after surgery. The median duration of general anesthesia (GA) was 1.72 h, with an interquartile range (IQR) of 1.17–2.52 h. The median duration of regional anesthesia was 1.54 h, with an IQR of 1.20–2.03 h. Anesthesia type and the duration of regional anesthesia were not associated with PE occurrence (adjusted odds ratio [aOR] [95% confidence interval, CI], 1.148 [0.671–2.098], p = 0.631), (aOR [95% CI], 1.117 [0.498–1.557], p = 0.738). The rates of PE consistently increased with GA prolongation (aOR [95% CI], 1.308 [1.176–1.432], p < 0.001). Compared with GA durations < 3 h, prolonged anesthesia was significantly associated with increased PE incidence (aOR [95% CI], 4.398 [2.585–7.565], p < 0.001). These findings were also confirmed by sensitivity analyses. Our study demonstrates that prolonged GA, particularly > 3 h, significantly increases the risk of PE.

BackgroundOrthodontic systematic reviews (SRs) use different methods to pool the individual studies in a meta-analysis when indicated. However, the number of studies included in orthodontic meta-analyses is relatively small. This study aimed to evaluate the direction of estimate changes of orthodontic meta-analyses (MAs) using different between-study variance methods considering the level of heterogeneity when few trials were pooled.MethodsSearch and study selection: Systematic reviews (SRs) published over the last three years, from the 1st of January 2020 to the 31st of December 2022, in six main orthodontic journals with at least one MA pooling five or lesser primary studies were identified. Data collection and analysis: Data were extracted from each eligible MA, which was replicated in a random effect model using DerSimonian and Laird (DL), Paule–Mandel (PM), Restricted maximum-likelihood (REML), Hartung Knapp and Sidik Jonkman (HKSJ) methods. The results were reported using median and interquartile range (IQR) for continuous data and frequencies for categorical data and analyzed using non-parametric tests. The Boruta algorithm was used to assess the significant predictors for the significant change in the confidence interval between the different methods compared to the DL method, which was only feasible using the HKSJ method.Results146 MAs were included, most applying the random effect model (n = 111; 76%) and pooling continuous data using mean difference (n = 121; 83%). The median number of studies was three (range 2, 4), and the overall statistical heterogeneity (I2 ranged from 0 to 99% with a median of 68%). Close to 60% of the significant findings became non-significant when HKSJ was applied compared to the DL method and when the heterogeneity was present I2>0%. On the other hand, 30.43% of the non-significant meta-analyses using the DL method became significant when HKSJ was used when the heterogeneity was absent I2 = 0%.ConclusionOrthodontic MAs with few studies can produce different results based on the between-study variance method and the statistical heterogeneity level. Compared to DL, HKSJ method is overconservative when I2 is greater than 0% and may result in false positive findings when the heterogeneity is absent.

Absolute and relative frequencies of the significance of the meta-analyses using the four different heterogeneity methods.

ObjectivesBrentuximab Vedotin (BV) is a novel antibody-drug conjugate (ADC) approved for the treatment of classical Hodgkin’s lymphoma and systemic anaplastic large cell lymphoma. However, as a relatively new therapeutic agent, the long-term safety profile and adverse event (AE) profile of BV require further investigation. This study aimed to identify significant and unexpected AEs associated with BV using data from the FDA Adverse Event Reporting System (FAERS) and the Japanese Adverse Drug Event Report (JADER) databases.MethodsData on BV-related AEs were extracted from the FAERS and JADER databases. Signal detection was performed using the reporting odds ratio (ROR) and 95% confidence intervals (95% CI). Risk signals were categorized according to system organ classes (SOCs) and preferred terms (PTs) as defined by the Medical Dictionary for Regulatory Activities (MedDRA) version 26.0. In addition, the onset times of BV-related AEs were analyzed.ResultsBetween 2004 and 2023, a total of 19,279 and 2,561 AEs related to BV were recorded in the FAERS and JADER databases, respectively. At the SOC level, prominent signals in the FAERS database included blood and lymphatic system disorders, benign, malignant, and unspecified neoplasms (including cysts and polyps), as well as congenital, familial, and genetic disorders. In the JADER database, the most notable signals involved benign, malignant, and unspecified neoplasms, blood and lymphatic system disorders, and nervous system disorders. At the PT level, the top five signals in the FAERS database were peripheral motor neuropathy, peripheral sensory neuropathy, pneumocystis jirovecii pneumonia, febrile bone marrow aplasia, and polyneuropathy. Unexpected AEs included febrile bone marrow aplasia and Guillain-Barré syndrome. In the JADER database, the top five signals included peripheral motor neuropathy, peripheral sensory neuropathy, bacterial gastroenteritis, febrile neutropenia and pneumonia, with unexpected AEs such as left ventricular dysfunction, cardiomegaly, retinal detachment, and marasmus. The median onset time of AEs was 22 days (interquartile range [IQR] 7–81 days) in FAERS and 27 days (IQR 7–73 days) in JADER.ConclusionThe signal detection results from the FAERS and JADER databases highlight the importance of monitoring significant and unexpected AEs associated with BV, particularly in the early stages of treatment. These findings contribute to enhancing the post-marketing safety profile of BV and offer valuable insights for clinical risk management strategies.

ObjectiveThis study investigated the effect of residual stenosis after carotid artery stenting (CAS) on periprocedural and long-term outcomes.MethodsPatients treated with CAS for symptomatic or asymptomatic carotid arterial stenosis were consecutively enrolled. Residual stenosis was estimated from post-procedure angiography findings. The effects of residual stenosis on 30-day periprocedural outcome and times to restenosis and clinical outcome were analyzed using logistic regression models and Wei-Lin-Weissfeld models, respectively.ResultsA total of 412 patients (age, 64.7 ± 17.0 years; male, 82.0%) were enrolled. The median baseline stenosis was 80% (interquartile range [IQR], 70–90%), which improved to 10% (0–30%) for residual stenosis. Residual stenosis was significantly associated with periprocedural outcome (adjusted odds ratio, 0.983; 95% confidence interval [CI], 0.965–0.999, P = 0.01) after adjustment for baseline stenosis, age, hypertension, symptomaticity, and statin use. Over the 5-year observation period, residual stenosis did not increase the global hazard for restenosis and clinical outcome (adjusted hazard ratio, 1.011; 95% CI, 0.997–1.025. In the event-specific model, residual stenosis increased the hazard for restenosis (adjusted hazard ratio, 1.041; 1.012–1.072) but not for clinical outcome (adjusted hazard ratio, 1.011; 0.997–1.025).ConclusionsResidual stenosis after carotid artery stenting may be useful to predict periprocedural outcome and restenosis.

Ranking of the first four study designs to address the types of question in which consensus was reached in stage 1.

The accurate calculation of reaction-free energies (ΔrG°) for diboronic acids and carbohydrates is challenging due to reactant flexibility and strong solute–solvent interactions. In this study, these challenges are addressed with a semiautomatic workflow based on quantum chemistry methods to calculate conformational free energies, generate microsolvated solute structural ensembles, and compute ΔrG°. Workflow parameters were optimized for accuracy and precision while controlling computational costs. We assessed the accuracy by studying three reactions of diboronic acids with glucose and galactose, finding that the conformational entropy contributes significantly (by 3–5 kcal/mol at room temperature). Explicit solvent molecules improve the computed ΔrG° accuracy by about 4 kcal/mol compared to experimental data, though using 13 or more water molecules reduced precision and increased computational overhead. After fine-tuning, the workflow demonstrated remarkable accuracy, with an absolute error of about 2 kcal/mol compared to experimental ΔrG° and an average interquartile range of 2.4 kcal/mol. These results highlight the workflow’s potential for designing and screening tweezer-like ligands with tailored selectivity for various carbohydrates.

BackgroundTrauma is a major global public health issue, with an annual death toll of approximately 5 million, disproportionately affecting low- and middle-income countries. Zambia bears a significant burden of trauma-related mortalities, contributing to 7% of all annual deaths and 1 in 5 premature deaths in the country. Despite the significant burden of trauma in our country, few studies have been conducted, with most focusing on high-population centers, and there is a lack of epidemiological data on trauma-related deaths in our region. Therefore, our aim was to estimate the proportion of deaths caused by injuries at Livingstone University Teaching Hospital, a tertiary hospital located in Zambia’s southern province.MethodsWe conducted a retrospective cross-sectional study from June 22, 2020, to June 22, 2021, among 956 individuals from 1 month old (29 days of age) to 100 years. Demographic and clinical data were collected from patient’s records from Accident and Emergency department. Data analysis included descriptive statistics, chi square, mann-whitney test and multivariable logistic using forward stepwise generalized linear model equations (GLM) to identified factors associated with mortality, with a significance level set at p < 0.05. Data were analyzed using STATA version 15.ResultsAmong the study participants, the median age was 26 years (interquartile range (IQR) 15, 37) and the majority were males (74.2%, n = 709). Prevalence of mortality was 1.0% (n = 10). The deaths were caused by burns (60%, n = 6), violence (30%, n = 3), and traffic accidents (10%, n = 1). Among those who died, the majority of the trauma occurred at home (90%, n = 9), followed by road (10%, n = 1) and were as a result of burns (60%, n = 6) and community violence (30%, n = 3). Survivors had significantly higher treatment costs (ZMK 9,837 vs. ZMK 6,037, p

Patient characteristics by race. Shown are the number and percentage of patients in each level for categorical variables, and the Median and Interquartile Range (IQR) for continuous variables in patients of White race versus Black race.