Off-Pump Coronary Artery Bypass Grafting in Patients with Left Ventricular Dysfunction: Short-Term Results from a Single Center in Bangladesh

Background: Off-pump coronary artery bypass grafting (OPCAB) is considered a safer alternative to on-pump surgery, especially in patients with left ventricular dysfunction (LVD). Objectives: This study assessed short-term outcomes and functional improvements in LVD patients post-OPCAB. Methods: The study included 200 coronary artery disease patients who underwent isolated off-pump coronary artery bypass grafting (OPCAB) at the National Heart Foundation Hospital and Research Institute between January 2019 and June 2020. Patients were categorized into Group 1, with a left ventricular ejection fraction (LVEF) of 30% - 39%, and Group 2, with an LVEF of 40% or higher. Echocardiographic assessments of left ventricular dimensions and ejection fraction were performed preoperatively, at discharge, and one month postoperatively. Results: In Group 1, preoperative left ventricular internal dimensions during diastole (LVIDd) and systole (LVIDs) were 53.48 ± 4.40 mm and 44.23 ± 3.93 mm, respectively, with a left ventricular ejection fraction (LVEF) of 35.28% ± 2.26%. At discharge, these values improved to 51.58 ± 4.04 mm (LVIDd), 41.23 ± 5.30 mm (LVIDs), and 39.25% ± 3.75% (LVEF). One month postoperatively, further improvements were observed: 46.29 ± 3.76 mm (LVIDd), 37.45 ± 3.68 mm (LVIDs), and 43.22% ± 4.67% (LVEF). Group 2 showed similar positive outcomes, with preoperative values of 47.09 ± 5.06 mm (LVIDd), 35.11 ± 5.25 mm (LVIDs), and 50.13% ± 7.25% (LVEF), improving to 42.37 ± 4.18 mm (LVIDd), 31.05 ± 4.19 mm (LVIDs), and 55.33% ± 7.05% (LVEF) at one month postoperatively. Both groups demonstrated significant improvements in left ventricular function and NYHA class, with most patients moving from class III/IV to I/II. Complications were minimal, and no mortality was observed. Conclusion: OPCAB is safe and effective for patients with LVEF 30% - 39% and LVEF ≥ 40%, providing significant short-term functional improvements without increased risk.

Mid-term survival of the Optimys short stem: A prospective case series of 500 patients

BACKGROUND In recent years,there has been an increase in the number of total hip arthroplasty procedures in the younger patient population.This active group has higher expectations of their prosthesis in comparison to the older population,and there is a greater physical demand for the prosthesis.Short femoral stems were in-troduced to retain proximal bone stock and joint biomechanics and became more common to implant in this specific population.Currently,the long-term survival and functional outcomes of various short stems are still being investigated in different clinics.AIM To determine the 5-year survival of the Optimys hip stem.METHODS This was a prospective multicenter cohort study of 500 patients conducted in two hospitals in the Netherlands.All patients received the Optimys short stem(Mathys Ltd,Bettlach,Switzerland).The primary outcome measure was survival of the hip stem,with revision as the endpoint.The secondary outcome measurements included patient-reported outcome measures(PROMs).Kaplan-Meier analysis was used to calculate the 5-year survival rate.Log-minus-log transformation was performed to calculate the 95%confidence interval(95%CI).Mixed model analyses were performed to assess the course of the PROMs during the 1st 2 years after surgery.Analyses were modeled separately for the 1st and 2nd years to calculate the yearly change in PROMs during both follow-up periods with accompanying 95%CIs.RESULTS The mean age of the total 500 patients was 62.3 years(standard deviation:10.6)and 202 were male(40%).At a median follow-up of 5.5 years(interquartile range:4.5-6.7),7 patients were deceased and 6 revisions were registered,for infection(n=3),subsidence(n=2)and malposition(n=1).This resulted in an overall 5-year survival of 98.8%(95%CI:97.3-99.5).If infection was left out as reason for revision,a stem survival of 99.4%(95%CI:98.1-99.8)was seen.Baseline questionnaires were completed by 471 patients(94%),317 patients(63%)completed the 1-year follow-up questionnaires and 233 patients(47%)completed the 2-year follow-up.Both outcome measures significantly improved across all domains in the 1st year after the operation(P<0.03 for all domains).In the 2nd year after surgery,no significant changes were observed in any domain in comparison to the 1-year follow-up.CONCLUSION The Optimys stem has a 5-year survival of 98.8%.Patient-reported outcome measures increased significantly in the 1st postoperative year with stabilization at the 2-year follow-up.

Navigating Long-Term Management Challenges in Short Bowel Syndrome: A Case Report of Chronic Intestinal Failure Complicated by Kidney Dysfunction

The most common cause of intestinal failure is short bowel syndrome (SBS), occurring as a result of a small functional intestine length, usually less than 200 cm, leading to intestinal malabsorption. A 59-year-old female with a past medical history of Crohns disease status post total colectomy with ileostomy over 20 years ago came to the hospital due to progressive weakness. Despite medical management, the patient had high ileostomy output, leading to electrolyte disbalance, metabolic acidosis, dehydration, and progressive kidney decline. Due to the high dependence on continuous fluid supplementation, it was decided to place a port for parenteral hydration to maintain fluid replacements and homeostasis after discharge. Prompt initiation of parenteral fluid replacement and close follow-up on patients with ileostomy and intestinal failure is strongly recommended to avoid complications and prevent intestinal, liver, or kidney transplants.

Prediction of Attention and Short-Term Memory Loss by EEG Workload Estimation

Mental workload plays a vital role in cognitive impairment. The impairment refers to a person’s difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that seriously affect everyday life. In this paper, the simultaneous capacity (SIMKAP) experiment-based EEG workload analysis was presented using 45 subjects for multitasking mental workload estimation with subject wise attention loss calculation as well as short term memory loss measurement. Using an open access preprocessed EEG dataset, Discrete wavelet transforms (DWT) was utilized for feature extraction and Minimum redundancy and maximum relevancy (MRMR) technique was used to select most relevance features. Wavelet decomposition technique was also used for decomposing EEG signals into five sub bands. Fourteen statistical features were calculated from each sub band signal to form a 5 × 14 window size. The Neural Network (Narrow) classification algorithm was used to classify dataset for low and high workload conditions and comparison was made using some other machine learning models. The results show the classifier’s accuracy of 86.7%, precision of 84.4%, F1 score of 86.33%, and recall of 88.37% that crosses the state-of-the art methodologies in the literature. This prediction is expected to greatly facilitate the improved way in memory and attention loss impairments assessment.

Short Term Traffic Flow Prediction Using Hybrid Deep Learning

Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential features.Secondly, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error value.An extensive experimental analysis was performed on the benchmark dataset.The evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.

Predicting and Curing Depression Using Long Short Term Memory and Global Vector

In today’s world, there are many people suffering from mentalhealth problems such as depression and anxiety. If these conditions are notidentified and treated early, they can get worse quickly and have far-reachingnegative effects. Unfortunately, many people suffering from these conditions,especially depression and hypertension, are unaware of their existence until theconditions become chronic. Thus, this paper proposes a novel approach usingBi-directional Long Short-Term Memory (Bi-LSTM) algorithm and GlobalVector (GloVe) algorithm for the prediction and treatment of these conditions.Smartwatches and fitness bands can be equipped with these algorithms whichcan share data with a variety of IoT devices and smart systems to betterunderstand and analyze the user’s condition. We compared the accuracy andloss of the training dataset and the validation dataset of the two modelsnamely, Bi-LSTM without a global vector layer and with a global vector layer.It was observed that the model of Bi-LSTM without a global vector layer hadan accuracy of 83%,while Bi-LSTMwith a global vector layer had an accuracyof 86% with a precision of 86.4%, and an F1 score of 0.861. In addition toproviding basic therapies for the treatment of identified cases, our model alsohelps prevent the deterioration of associated conditions, making our methoda real-world solution.

Ankle Fractures, Short Term Operative Outcome: A Retrospective Case Series

Introduction and Background: Ankle injury is one of the most frequent presenting injuries to the emergency room and ankle fractures are common fracture in the lower limbs injuries that may require operative treatment with variable outcomes. Materials and Methods: Sixty-three patients were included in my retrospective study, and all with a displaced fracture of the ankle caused by high energy trauma were treated by open reduction and rigid internal fixation. Results: After follow-up at six weeks and twelve weeks, the results were satisfactory in fifty-five percent out of all the sixty-three patients. Conclusions and Recommendations: Ankle fractures occur mainly in young males of the age group between 26 and 35 years, mostly caused by fall down and motor vehicle accident.

Snowmelt Flood Mapping and Land Surface Short-Term Dynamics Assessment in a “Before-During-After” Scenario Based on Radar and Optical Satellite Imagery: Case Study Around the Lewisville Lake (Dallas/Fort Worth Metropolitan, Texas, USA)

The main goal of this study has been to map flood and assess land surface short-term dynamics in relation with snowy weather. The two recent snowfall events, which happened in, February 14th and 15th, of year 2021, and February 3rd and 4th, of year 2022, were chosen. A pre-analysis correlation was assumed between, the snow events, recurrency of floods, and changes in the land surface characteristics (i.e., wetness, energy, temperature), in a “Before-During-After” scenario. Active and passive microwave satellites data such as, Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral instrument (MSI) and Landsat-9 Operation Land Imager-2/Thermal Infrared Sensors-2 (OLI-2/TIRS-2), as well as cloud databased global models for water and urban layers were used. The first step of processing was thresholding of SAR image, at 0.25 cutoff, based on bimodal histogram distribution, followed by the change analysis. The following processing consisted in the images transformation, by computing the tasseled cap transformation wetness (TCTw) and the surface albedo on MSI image. In addition, the land surface temperature (LST) was modeled from OLI-2/TIRS-2 image. Then, a 5th order polynomial regression was computed, between TCTw as dependent variable and, albedo and LST as independent variables. As a first result, an area of 5.6 km2 has been mapped as recurrently flooded from the two years assessment. The other output highlighted a constant increase of wetness (TCTw), considered most influential on land surface dynamics, comparatively to energy exchange (albedo) and temperature (LST). The “After” event dependency between the three indicators was highest, with a correlation coefficient, R2 = 0.682, confirming the persistence of wetness after-snowmelt. Validation over topographic layers confirmed that, recurrently flooded areas are mostly distributed on, lowest valley depth points, farthest distances from channel network (i.e., from perennial waters), and lowest relative slope position areas. Whereas, 88.9% of the validation sampling were confirmed in the laboratory, and 86.7% of urban validation points were assessed as recurrently flooded when combining pre-/post-field-work campaign.

Short-TermWind Power Prediction Based on Combinatorial Neural Networks

Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation.Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections.For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model,the short-term prediction of wind power based on a combined neural network is proposed.First,the Bi-directional Long Short Term Memory(BiLSTM)network prediction model is constructed,and the bi-directional nature of the BiLSTM network is used to deeply mine the wind power data information and find the correlation information within the data.Secondly,to avoid the limitation of a single prediction model when the wind power changes abruptly,the Wavelet Transform-Improved Adaptive Genetic Algorithm-Back Propagation(WT-IAGA-BP)neural network based on the combination of the WT-IAGA-BP neural network and BiLSTM network is constructed for the short-term prediction of wind power.Finally,comparing with LSTM,BiLSTM,WT-LSTM,WT-BiLSTM,WT-IAGA-BP,and WT-IAGA-BP&LSTM prediction models,it is verified that the wind power short-term prediction model based on the combination of WT-IAGA-BP neural network and BiLSTM network has higher prediction accuracy.

Short-Term Prediction of Photovoltaic Power Generation Based on LMD Permutation Entropy and Singular Spectrum Analysis

The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.

Identification of the Breach of Short-Term Rental Regulations in Irish Rent Pressure Zones

The housing crisis in Ireland has rapidly grown in recent years. To make a more significant profit, many landlords are no longer renting out their houses under long-term tenancies but under short-term tenancies. Regulating rentals in Rent Pressure Zones with the highest and rising rents is becoming a tricky issue. In this paper, we develop a breach identifier to check short-term rentals located in Rent Pressure Zones with potential breaches only using publicly available data from Airbnb (an online marketplace focused on short-term home-stays) and Irish government websites. First, we use a Residual Neural Network to filter out outdoor landscape photos that negatively impact identifying whether an owner has multiple rentals in a Rent Pressure Zone. Second, a Siamese Neural Network is used to compare the similarity of indoor photos to determine if multiple rental posts correspond to the same residence. Next, we use the Haversine algorithm to locate short-term rentals within a circle centered on the coordinate of a permit. Short-term rentals with a permit will not be restricted. Finally, we improve the occupancy estimation model combined with sentiment analysis, which may provide higher accuracy.

Short-Term Memory Capacity across Time and Language Estimated from Ancient and Modern Literary Texts. Study-Case: New Testament Translations

We study the short-term memory capacity of ancient readers of the original New Testament written in Greek, of its translations to Latin and to modern languages. To model it, we consider the number of words between any two contiguous interpunctions Ip, because this parameter can model how the human mind memorizes “chunks” of information. Since IP can be calculated for any alphabetical text, we can perform experiments—otherwise impossible— with ancient readers by studying the literary works they used to read. The “experiments” compare the IP of texts of a language/translation to those of another language/translation by measuring the minimum average probability of finding joint readers (those who can read both texts because of similar short-term memory capacity) and by defining an “overlap index”. We also define the population of universal readers, people who can read any New Testament text in any language. Future work is vast, with many research tracks, because alphabetical literatures are very large and allow many experiments, such as comparing authors, translations or even texts written by artificial intelligence tools.

Comparative study on the performance of ConvLSTM and ConvGRU in classification problems-taking early warning of short-duration heavy rainfall as an example

卷积长短期记忆单元ConvLSTM和卷积门控循环单元ConvGRU是两种广泛应用的深度学习单元,通过将循环机制与卷积运算相结合,常常用于时空序列的预测.为了明确上述两种模型的收敛速度和分类能力,需要使用相同的模型架构对相同的分类问题进行预测.本研究将北京短时强降水区级预警问题看作深度学习中的二分类问题,使用京津冀雷达网的组合反射率数据和北京区域内的自动气象站降雨数据进行深度学习模型的训练和评估.结果表明,ConvGRU的收敛速度比ConvLSTM快约25%.ConvLSTM和ConvGRU的预警性能随地区,时间,降雨强度的变化趋势相似,但大部分ConvLSTM的得分较高,少数情况下ConvGRU的得分较高.

Summary of Short-vine Watermelon Breeding Practice

This paper expounded the current situation and genetic mechanisms of short-vine watermelon breeding from the aspects of material sources,breeding process and genetic characteristics of F_1,hoping to provide a theoretical basis for short-vine watermelon breeding,and breeding materials for watermelon planting innovation,as well as new opportunities for high-quality and high-yield watermelon.

The Effects of Accumulated Short Bouts of Mobile-Based Physical Activity Programs on Depression,Perceived Stress,and Negative Affectivity among College Students in South Korea:Quasi-Experimental Study

Regular physical activity(PA)is known to enhance multifaceted health benefits,including both physical and mental health.However,traditional in-person physical activity programs have drawbacks,including time constraints for busy people.Although evidence suggests positive impacts on mental health through mobile-based physical activity,effects of accumulated short bouts of physical activity using mobile devices are unexplored.Thus,this study aims to investigate these effects,focusing on depression,perceived stress,and negative affectivity among South Korean college students.Forty-six healthy college students were divided into the accumulated group(n=23,female=47.8%)and control group(n=23,female=47.6%).The accumulated group engaged in mobile-based physical activity,following guidelines to accumulate a minimum of two times per day and three times a week.Sessions were divided into short bouts,ensuing each bout lasted at least 10 min.The control group did not engage in any specific physical activity.The data analysis involved comparing the scores of the intervention and control groups using several statistical techniques,such as independent sample t-test,paired sample t-tests,and 2(time)×2(group)repeated measures analysis of variance.The demographic characteristics at the pre-test showed no statistically significant differences between the groups.The accumulated group had significant decreases in depression(t_(40)=2.59,p=0.013,Cohen’s D=0.84)and perceived stress(t_(40)=2.06,p=0.046,Cohen’s D=0.56)from the pre-to post-test.The control group exhibited no statistically significant differences in any variables.Furthermore,there were significant effects of time on depression scores(F1,36=4.77,p=0.036,η_(p)^(2)=0.12)while significant interaction effects were also observed for depression(F_(1,36)=6.59,p=0.015,η_(p)^(2)=0.16).This study offers informative insights into the potential advantages of mobile-based physical activity programs with accumulated periods for enhancing mental health,specifically in relation to depression.This study illuminates the current ongoing discussions on efficient approaches to encourage mobile-based physical activity and improve mental well-being,addressing various lifestyles and busy schedules.

Docking of Human Band 3 Anion Transporter Proteins with Their Plasmodium falciparum Interactors Based on Short Linear Motifs

Plasmodium (P.) falciparum is a pathogen that causes severe forms of malaria. Protein interactions have been shown to occur between P. falciparum and human erythrocytes in human blood. The Band 3 Anion Transporter (B3AT) protein is considered the main invasive pathway for the parasite in erythrocytes that causes clinical symptoms for malaria in humans. The interactions between P. falciparum parasites and erythrocytes along this receptor have previously been explored. Short linear motifs (SLIMs) are short linear mediator sequences that involve several biological processes, acting as mediators of protein interactions identifiable by computational tools such as SLiMFinder. For a given protein, the identification of SLIMs allows predicting its interactors. Using the SLIMs approach, protein-protein interaction network analyses between P. falciparum and its human host, were used to identify a tryptophan-rich protein, A5K5E5_PLAVS as an essential interactor of B3AT. To better understand the interaction mechanism, a guided protein-protein docking approach based on SLIM motifs was performed for human B3AT and A5K5E5_PLAVS. The highlights of this important interaction between P. falciparum and its human host have the potential to pave the way to identify new therapeutic candidates.

Profile of Full-Term Births in Maternity Wards of Public Hospitals in Douala Cameroon

Introduction: Pregnancy as much as childbirth constitutes a risky situation, potentially fraught with sometimes dramatic complications: maternal death. Objective: We conducted this study with the aim of establishing the profile of those giving birth in our context with the aim to anticipate operationally in the future on morbidity but more on maternal deaths. Methodology: We conducted, using a structured questionnaire, a prospective descriptive study in representative maternity wards in the city of Douala;the study variables were socio-economic, anthropometric, obstetrical and clinical. Statistical analyses were carried out with CS Pro 7.3 and SPSS version 25.0 software. The Student, Chi-square and Fischer tests were used to compare the means of the variables and the percentages. Results: We recruited 305 births for our study. The average age of our births was 28.7 years ± 6.1 with an average height of 161.6 cm ± 5.06;an average body mass index at the start of pregnancy of 28.0 kilograms/square meter and 31.3 kilograms/square meter at delivery;the average weight gain was 8.4 g ± 5.37;an average gestation of 2.84±1.90;an average parity of 2.2 ± 2.1 with an average birth interval of 27.7 months ± 23.7. The average gestational age was 39.2 weeks ± 1.21 with pregnancy pathology dominated by malaria;85.9% began their prenatal follow-up before the 14th week of amenorrhea. Conclusion: The profile of childbirth in urban Cameroon does not seem potentially dystocic compared to that of the same regional and racial area.

A Novel Method for Forecasting Freak Wave Occurrence in A Short Crested Coastal Sea

In order to forecast the distribution of crest amplitudes and the occurrence of freak waves in a short crested coastal sea,a novel transformed linear simulation method is initially proposed in this paper.A Hermite transformation model expressed as a monotonic cubic polynomial serves as the foundation for the novel simulation technique.The wave crest amplitude exceedance probabilities of two sea states-one with a directional wave spectrum based on the measured wave elevation data at the Yura coast and the other with a typical directional JONSWAP wave spectrum-have been predicted using the novel simulation method that has been proposed.The likelihood that a particular critical wave crest amplitude will be exceeded is directly correlated with the probability that freak waves will occur.It is shown that the novel simulation approach suggested can provide predictions that are more precise than those obtained from the Rayleigh crest amplitude distribution model,the Jahns and Wheeler crest amplitude distribution model,or the conventional linear simulation method.This study also demonstrated that the nonlinear simulation method is less effective than the novel simulation method in terms of efficiency.

A Comparison between Late Preterm and Term Infants with Respiratory Distress Syndrome, Early-Onset Sepsis, and Neonatal Jaundice in Ecuadorian Newborns

Background: To examine the differences in prevalence of respiratory distress syndrome, early-onset sepsis and jaundice, between late preterm infants versus term infants in Ecuadorian newborns. Methods: Study design: Epidemiological, observational, and cross-sectional, with two cohorts of patients. Settings: IESS Quito Sur Hospital at Quito, Ecuador, from February to April of 2020. Participants: This study included 204 newborns, 102 preterm infants, 102 term infants. Results: There are significant differences between late preterm infants and term infants, with a p-value of 0.000 in the prevalence of early sepsis, 70.59% vs. 35.29%. In respiratory distress syndrome between late and term premature infants, significant differences were observed with a p-value of 0.000, the proportion being 55.58% vs. 24.51% respectively. The prevalence of jaundice is higher in term infants with a p value of 0.002, 72.55%, versus 51.96% in late preterm infants, and the mean value of bilirubins in mg/dL was higher in term infants 14.32 versus 12.33 in late preterm infants;this difference is statistically significant with a p value of 0.004. Admission to the NICU is more frequent in late preterm infants with a p-value of 0.000, being 42.16% for late preterm infants vs. 7.84% in term infants;the mean of the hospital days with p-value 0.005, was higher in late preterm infants 4.97 days vs. 3.55 days for term newborns. Conclusion: Due to the conditions of their immaturity, late preterm infants are 2.86 times more likely to present early sepsis than full-term newborns. It is shown that late preterm infants are 2.69 times more likely to have respiratory distress syndrome compared to term infants, therefore, late preterm infants have a longer hospital stay of 4.97 days versus 3.55 days in term infants. Jaundice and mean bilirubin levels are higher in term infants due to blood group incompatibility and insufficient breastfeeding.

Long-term assessment of collagenase treatment for Dupuytren’s contracture:A 10-year follow-up study

BACKGROUND Enzymatic fasciotomy with collagenase clostridium histolyticum(CCH)has revolutionized the treatment for Dupuytren’s contracture(DC).Despite its benefits,the long-term outcomes remain unclear.This study presented a comprehensive 10-year follow-up assessment of the enduring effects of CCH on patients with DC.AIM To compare the short-term(12 wk)and long-term(10 years)outcomes on CCH treatment in patients with DC.METHODS A cohort of 45 patients was treated with CCH at the metacarpophalangeal(MCP)joint and the proximal interphalangeal(PIP)joint and underwent systematic reevaluation.The study adhered to multicenter trial protocols,and assessments were conducted at 12 wk,7 years,and 10 years post-surgery.RESULTS Thirty-seven patients completed the 10-year follow-up.At 10 years,patients treated at the PIP joint exhibited a 100%recurrence.However,patients treated at the MCP joint only showed a 50%recurrence.Patient satisfaction varied,with a lower satisfaction reported in PIP joint cases.Recurrence exceeding 20 degrees on the total passive extension deficit was observed,indicating a challenge for sustained efficacy.Significant differences were noted between outcomes at the 7-year and 10-year intervals.CONCLUSION CCH demonstrated sustained efficacy when applied to the MCP joint.However,caution is warranted for CCH treatment at the PIP joint due to a high level of recurrence and low patient satisfaction.Re-intervention is needed within a decade of treatment.