Continuous Data Research Articles

Sensitivity of surface downward longwave radiation to aerosol optical depth over the Lake Taihu region, China

Downward longwave radiation (DLR) is an important component of the global radiation budget, significantly influencing surface energy dynamics. Accurate quantification of aerosol impacts on surface DLR is crucial for refining numerical weather predictions and comprehending surface energy balance. While the impact of aerosols on downward shortwave radiation has garnered substantial attention, investigation into the longwave radiation effect of aerosols remains relatively limited. In this study, the impact of aerosols on surface DLR is quantified by using continuous radiation data, conventional meteorological observations, and aerosol optical depth (AOD) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. These datasets span the period from 2011 to 2018 and are obtained from six stations of the Lake Taihu Flux Observational Network situated in Eastern China. The sensitivity of DLR to AOD (dDLR/dAOD) is determined through multiple linear regressions using both observational data and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Additionally, a radiative transfer model is employed to derive the dDLR/dAOD. A three-variable linear regression of the observational data shows that DLR increases by 4.66 ± 1.99 W m−2 per unit increase in AOD (mean ± 1 standard deviation) under clear-sky conditions. The radiative transfer model and the three-variable linear regression of MERRA-2 reanalysis yield similar sensitivity values of 4.69 ± 1.23 W m−2 AOD−1 and 4.10 ± 0.25 W m−2 AOD−1, respectively. However, these sensitivity values are considerably smaller than those reported in previous studies. Plausible factors contributing to the observed variance in sensitivity values are discussed. These results help to improve our understanding on the aerosol longwave radiation effect over the Lake Taihu region in Eastern China.

Prophylactic-dose direct oral anticoagulants for non-hospitalised people with COVID-19: A meta-analysis of randomised controlled trials.

Several reviews have been conducted on thromboprophylaxis in non-hospitalised patients with coronavirus disease 2019 (COVID-19). In this systematic review and meta-analysis, we sought to investigate the impact of prophylactic-dose direct oral anticoagulants (DOACs) in this population. We searched PubMed, Web of Science, EMBASE and Cochrane Library for randomised controlled trials (RCTs) comparing prophylactic-dose DOACs with placebo or no treatment in non-hospitalised patients with COVID-19 until September 2023. The primary efficacy outcome was a composite of all-cause mortality and thromboembolic events, while major bleeding events were the primary safety outcome. We expressed continuous outcome data as mean differences (MDs) with 95% confidence intervals (CIs) and dichotomous outcome data as risk ratios (RRs) with 95% CIs. We included six RCTs involving 4307 patients. Prophylactic-dose DOAC therapy compared with placebo or no treatment was associated with significantly decreased risks of the composite outcome of all-cause mortality and thromboembolic events (1.43% vs 2.67% (RR = 0.53; 95% CI = 0.34-0.82, P = 0.004, I2 = 3%)). Major bleeding events were infrequent, and we detected no significant differences between patients assigned to prophylactic-dose DOACs vs placebo or no treatment (0.19% vs 0.05% (RR = 2.50; 95% CI = 0.49-12.87, P = 0.27, I2 = 0%)). The use of prophylactic-dose DOACs was also associated with a reduction in venous thromboembolism, with no difference in all-cause mortality, arterial thromboembolism, hospitalisations, and clinically relevant nonmajor bleeding between two groups. Sensitivity analyses with the leave-one-out method for the primary efficacy and safety outcome did not change the effect estimate substantially. We found that prophylaxis-dose DOACs could significantly improve clinical outcomes and reduce venous thrombotic events without increasing the risk of major bleeding events compared with placebo or no treatment in non-hospitalised patients with COVID-19. PROSPERO: CRD42023466889.

Rural-Based Primary School Teachers’ Inculcated Servant Leadership Skills and their Enhanced Capacity of Addressing Rural Education Challenges through Edu-Heroes Initiative in Tanzania

ABSTRACT This article reports the findings of a qualitative tracer study that was conducted for teachers in 13 rural-based primary schools in Tanzania. These teachers had benefited from a leadership training dubbed “Edu-heroes Initiative.” The project had been designed to enhance rural primary school teachers’ capacity to address rural education challenges through servant leadership. The tracing aimed to document their improvements evident in the actions they took in their respective rural schools, which were considered as primary units of analysis. The initiative had six phases – planning for training, participants’ selection, initial face-to-face training, participants’ deployment, and online post-trainings for a year, continuous data collection, and data analysis and report writing. Through thematic analysis, the tracer results show that the leadership trainings offered the necessary servant leadership skills to rural-based primary school teachers, who demonstrated enhanced capacity in finding local solutions to rural education challenges soon after their deployment. They engaged and created constructive connections with their students and other stakeholders in the pursuit of lasting solutions to the problems facing rural education. The article, therefore, recommends for scaling up of such continuous professional development to empower all rural-based teachers, with their innovative ideas deserving special attention and support from educational leaders and local communities for relevant and sustainable schools’ improvement

Medical Malpractice Cases Involving Medical Students.

This study uses a large national legal database to analyze characteristics of malpractice claims involving U.S. medical students. The Westlaw database was searched in September 2023 for malpractice cases involving medical students from January 1, 1900, through September 1, 2023. Each case was independently reviewed by 2 authors, abstracting each variable. Categorical data were summarized as frequency of occurrence (i.e., number and percentage), and continuous data were summarized with means, medians, and ranges. All authors independently reviewed the dataset to identify potential themes and codes. There were 65 cases that met the inclusion criteria. Reported patient outcomes were death (19, 29%), pain (25, 38%), and disability (36, 55%). The most common specialties involved were emergency medicine (16, 25%), general surgery (14, 22%), and obstetrics and gynecology (13, 20%). The most common primary alleged errors attributed to students related to medical decision-making (30, 46%), procedural complication (24, 37%), and poor communication (11, 17%). Among 23 (35%) cases reporting year of training, 1 (4%) included a second-year student, 13 (57%) included third-year students, and 9 (39%) included fourth-year students. Of the 65 lawsuits, 28 (43%) resulted in a settlement or verdict against the medical student. The total amount paid in these cases was $78,192,612, with a mean (median) of $3,007,408 ($1,050,000) per case. Of these 28 cases, 14 (50%) cited minimal or no physician supervision. Medical malpractice claims involving medical students are rare but commonly relate to medical decision-making, procedural complication, and poor communication, with a lack of supervision being frequently cited. These results can be used to guide students and supervising physicians on how to avoid scenarios that may increase vulnerability to medical malpractice lawsuits.

Household carbon monoxide (CO) concentrations in a large African city: An unquantified public health burden?

Carbon monoxide (CO) is a poisonous gas produced by incomplete combustion of carbon-based fuels that is linked to mortality and morbidity. Household air pollution from burning fuels on poorly ventilated stoves can lead to high concentrations of CO in homes. There are few datasets available on household concentrations of CO in urban areas of sub-Saharan African countries. CO was measured every minute over 24 h in a sample of homes in Nairobi, Kenya. Data on household characteristics were gathered by questionnaire. Metrics of exposure were summarised and analysis of temporal changes in concentration was performed. Continuous 24-h data were available from 138 homes. The mean (SD), median (IQR) and maximum 24-h CO concentration was 4.9 (6.4), 2.8 (1.0–6.3) and 44 ppm, respectively. 50% of homes had detectable CO concentrations for 847 min (14h07m) or longer during the 24-h period, and 9% of homes would have activated a CO-alarm operating to European specifications. An association between a metric of total CO exposure and self-reported exposure to vapours >15 h per week was identified, however this were not statistically significant after adjustment for the multiple comparisons performed. Mean concentrations were broadly similar in homes from a more affluent area and an informal settlement. A model of typical exposure suggests that cooking is likely to be responsible for approximately 60% of the CO exposure of Nairobi schoolchildren. Household CO concentrations are substantial in Nairobi, Kenya, despite most homes using gas or liquid fuels. Concentrations tend to be highest during the evening, probably associated with periods of cooking. Household air pollution from cooking is the main source of CO exposure of Nairobi schoolchildren. The public health impacts of long-term CO exposure in cities in sub-Saharan Africa may be considerable and should be studied further.

P161 Self-reported barriers to exercise from a national United Kingdom systemic lupus erythematosus cohort

Abstract Background/Aims Patients with systemic lupus erythematosus (SLE) are more inactive than the general population. Regular physical activity (PA) positively impacts many health parameters, including cardiovascular health. To better understand patient barriers to leisure time PA we conducted a national survey of patients with SLE in the UK. Methods An online survey was disseminated via LUPUS UK social media platforms between 5/05/2023 and 28/10/2023. Participants self-reported demographic data including postcodes. Indices of Multiple Deprivation (IMD) were used to define socioeconomic deprivation and were split into quantiles, where quartile 5 represents participants living in 20% least deprived areas in England. Categorical and continuous data were analysed using Fisher’s exact test and Mann-Whitney U test respectively. Results There were 249 individual responses. Table 1 describes the cohort demographics alongside response data. On a 0-10 scale (very unsatisfied to very satisfied), 171/244 (70%) of participants rated their satisfaction with their amounts of leisure time PA as ≤ 4. The most commonly reported barriers to PA were fatigue (221/249, 88.8%), pain (155/249, 62.2%) and lupus-related symptoms/flares (145/249, 58.2%). Dislike of exercise was reported by < 10% of cohort (22/249, 8.8%). When asked to identify the primary barrier to PA, 119/244 (48.8%) reported fatigue. Lack of motivation (40/244, 16.4%) and knowledge about exercise (18/244, 7.4%) were other predominant barriers. Concern around injury differed between ethnic groups (p = 0.016). Other barriers did not differ by sex, ethnic group or socioeconomic deprivation. Participants reporting lack of time were younger with a median age of 45 (35-53) versus 54 (43-63) years, p = <0.001. Patients reporting fatigue, pain and self-consciousness had higher BMI in kg/m2 (27.7 [23.6-32.3] vs 23.4 [20.8-28.2]; 28.7 [23.8-33.1] vs 25.4 [22.6-28.8]; 31.4 [27.9-38.4] vs 25.8 [23.1-30.2]), p = <0.001. Lupus-related symptoms/flares and concern that PA may worsen lupus were reported by participants with a shorter disease duration in years (8 [3-17] vs 12 [4.5-24] p = 0.002, and 7 [2-15] vs 10 [4.5-23] p = <0.001 respectively). Conclusion 70% of patients were dissatisfied with their level of leisure time PA. Fatigue and pain were the most commonly reported barriers to PA. Reported barriers differ between patients highlighting the need for personalised exercise programmes for patients with SLE. Disclosure A. Baranskaya: None. A.J. Wadley: None. J. Reynolds: None.

Impact of COVID-19 on Acute Care Hospitalizations for Suicidality.

The coronavirus disease 2019 (COVID-19) pandemic contributed to the public health crisis for pediatric mental health. We characterized our local patient population presenting with suicidality or suicide attempts before and after the pandemic by examining: 1. frequencies of hospitalizations for suicidality to determine whether they differed by age, legal sex, race and ethnicity, or socioeconomic status; 2. average length of stay and discharge disposition; 3. 7-, 30-, and 365-day reutilization rates; and 4. admission trends during COVID-19 surges. Retrospective data between March 2018 and March 2022 was analyzed, including patients ages 10 to 17 years hospitalized for either suicidality or a suicide attempt at 1 freestanding tertiary care pediatric medical hospital in the Midwest. Encounters were divided into 2 categories on the basis of the COVID-19 pandemic: "Prelockdown" (March 1, 2018-March 12, 2020) and "postlockdown" (March 13, 2020-March 31, 2022). Patients were limited to 1 presentation pre- and postlockdown. We analyzed frequencies using means and SDs, categorical data using χ2 and Fisher's exact tests, and continuous data with t tests. A total of 1017 encounters were included, stratified into pre- and postlockdown groups for analysis (909 encounters, 889 unique patients). There was a significant difference in 365-day reutilization pre- and postlockdown when analyzing re-presentation to the emergency department (P = .025) and hospital readmission (P = .006). Admissions incrementally increased after the COVID-19 alpha variants in September 2020 and again after the delta variant in August 2021. The COVID-19 pandemic further intensified the already critical issue of pediatric mental health, demonstrating increased reutilization in the year after their initial presentation and an increase in admissions after the alpha variant.

Developing kNN forest data imputation for Catalonia

The combined use of LiDAR (Light Detection And Ranging) scanning and field inventories can provide spatially continuous wall-to-wall information on forest characteristics. This information can be used in many ways in forest mapping, scenario analyses, and forest management planning. This study aimed to find the optimal way to obtain continuous forest data for Catalonia when using kNN imputation (kNN stands for “k nearest neighbors”). In this method, data are imputed to a certain location from k field-measured sample plots, which are the most similar to the location in terms of LiDAR metrics and topographic variables. Weighted multidimensional Euclidean distance was used as the similarity measure. The study tested two different methods to optimize the distance measure. The first method optimized, in the first step, the set of LiDAR and topographic variables used in the measure, as well as the transformations of these variables. The weights of the selected variables were optimized in the second step. The other method optimized the variable set as well as their transformations and weights in one single step. The two-step method that first finds the variables and their transformations and subsequently optimizes their weights resulted in the best imputation results. In the study area, the use of three to five nearest neighbors was recommended. Altitude and latitude turned out to be the most important variables when assessing the similarity of two locations of Catalan forests in the context of kNN data imputation. The optimal distance measure always included both LiDAR metrics and topographic variables. The study showed that the optimal similarity measure may be different for different regions. Therefore, it was suggested that kNN data imputation should always be started with the optimization of the measure that is used to select the k nearest neighbors.

A phylogenetic method linking nucleotide substitution rates to rates of continuous trait evolution.

Genomes contain conserved non-coding sequences that perform important biological functions, such as gene regulation. We present a phylogenetic method, PhyloAcc-C, that associates nucleotide substitution rates with changes in a continuous trait of interest. The method takes as input a multiple sequence alignment of conserved elements, continuous trait data observed in extant species, and a background phylogeny and substitution process. Gibbs sampling is used to assign rate categories (background, conserved, accelerated) to lineages and explore whether the assigned rate categories are associated with increases or decreases in the rate of trait evolution. We test our method using simulations and then illustrate its application using mammalian body size and lifespan data previously analyzed with respect to protein coding genes. Like other studies, we find processes such as tumor suppression, telomere maintenance, and p53 regulation to be related to changes in longevity and body size. In addition, we also find that skeletal genes, and developmental processes, such as sprouting angiogenesis, are relevant.

The Added Value of Prostate Magnetic Resonance Imaging to Patient Selection.

There has been a rapid increase in the utilization of magnetic resonance imaging (MRI) for prostate cancer detection. The objective of this study was to measure the increase in utilization of MRI before prostate biopsy and the effects on the distribution of Prostate Imaging Reporting and Data System (PI-RAD) scores and Gleason grades over a 5-year interval in an integrated health system. The authors conducted a retrospective analysis of prostate MRI studies prior to biopsy in the calendar years of 2017 and 2022. Peak PI-RADS score, peak Gleason grade of suspected prostatic lesions, and the number of biopsy cores were collected from radiology reports and pathology reports from patients' electronic health records, respectively. All statistical tests were 2-tailed with a significance level set at p < 0.05. Categorical data analyses were performed using Mann-Whitney tests. Continuous data analyses were performed using t-tests. The total number of prostate MRIs and the number of MRIs with subsequent biopsy respectively increased by 178% and 215% over a 5-year interval (2017-2022). There was a higher proportion of MRI studies with an associated biopsy given a PI-RADS score of ≥ 3 (91%) and a Gleason grade of ≥ 7 (61%) in 2022 than in 2017 (PI-RADS: 75%; Gleason: 28%). Increased utilization of prostate MRI has been associated with a higher proportion of biopsies with high PI-RADS and Gleason scores consistent with improved patient selection in this integrated health system.

[18F]fluorodeoxyglucose positron emission tomography/computed tomography in combination with clinical data in predicting overall survival in non-small-cell lung cancer patients: A retrospective study

IntroductionPositron emission tomography/computed tomography (PET/CT) has an established role in evaluating patients with lung cancer. The aim of this work was to assess the predictive capability of [18F]Fluorodeoxyglucose ([18F]FDG) PET/CT parameters on overall survival (OS) in lung cancer patients using an artificial neural network (ANN) in parallel with conventional statistical analysis. MethodsRetrospective analysis was performed on a group of 165 lung cancer patients (98M, 67F). PET features associated with the primary tumor: maximum and mean standardized uptake value (SUVmax, SUVmean), total lesion glycolysis (TLG) metabolic tumor volume (MTV) and area under the curve-cumulative SUV histogram (AUC-CSH) and metastatic lesions (SUVmaxtotal, SUVmeantotal, TLGtotal, and MTVtotal) were evaluated. In parallel with conventional statistical analysis (Chi-Square analysis for nominal data, Student's t test for continuous data), the data was evaluated using an ANN. There were 97 input variables in 165 patients using a binary classification of either below, or greater than/equal to median survival post primary diagnosis. Additionally, phantom study was performed to assess the most optimal contouring method. ResultsMales had statistically higher SUVmax (mean: 10.7 vs 8.9; p = 0.020), MTV (mean: 66.5 cm3 vs. 21.5 cm3; p = 0.001), TLG (mean 404.7 vs. 115.0; p = 0.003), TLGtotal (mean: 946.7 vs. 433.3; p = 0.014) and MTVtotal (mean: 242.0 cm3 vs. 103.7 cm3; p = 0.027) than females. The ANN after training and validation was optimised with a final architecture of 4 scaling layer inputs (TLGtotal, SUVmaxtotal, SUVmeantotal and disease stage) and receiving operator characteristic (ROC) analysis demonstrated an AUC of 0.764 (sensitivity of 92.3%, specificity of 57.1%). ConclusionConventional statistical analysis and the ANN provided concordant findings in relation to variables that predict decreased survival. The ANN provided a weighted algorithm of the 4 key features to predict decreased survival. Implication for practiceIdentification of parameters which can predict survival in lung cancer patients might be helpful in choosing the group of patients who require closer look during the follow-up.

Total Anomalous Pulmonary Venous Connection in Adults: Should We Offer Surgery?

Background: We aim to determine the surgical outcomes of adult patients with total anomalous pulmonary venous connection (TAPVC) and examine the regression of pulmonary artery (PA) pressures after the procedure. Methods: We reviewed the hospital records from 2003 to 2022 and identified 49 adult patients with TAPVC. We assessed their surgical outcomes and the trend of PA pressures after the procedure. Continuous data are presented as mean ± SD or median (interquartile range) and categorical variables are presented as percentages. Results: The median age of the patients was 23 years (range 18-42) and 31 (63.3%) were male. Thirty-six patients (73.5%) had supracardiac TAPVC. The mean systolic PA pressure was 65.8 ± 16.4 mm Hg and it decreased by 47.9% (34%, 61.8%) after surgery. Moderate or more tricuspid regurgitation was seen in 27 (55.1%) patients before surgery; however, it was present in only 3 (6.1%) patients during early follow-up. There was no intraoperative or 30-day mortality, and the median hospital length of stay was six days. Long-term follow-up data were available for 29 patients with the average duration of follow-up being 5.6 years (range 6 months to 15 years) and the mean systolic PA pressures of this cohort was 29.8±7.9 mm Hg. Forty-six (93.1%) patients were asymptomatic; four women had uneventful pregnancies and delivered healthy children. Conclusion: Surgical repair of the naturally selected group of adult TAPVC patients can be performed safely with good results. Regression in flow-related pulmonary hypertension and an improvement in functional quality of life are seen in nearly all patients.

Corrigendum to a fractional sideways problem in a one‐dimensional finite‐slab with deterministic and random interior perturbed data

In a lot of engineering applications, one has to recover the temperature of a heat body having a surface that cannot be measured directly. This raises an inverse problem of determining the temperature on the surface of a body using interior measurements, which is called the sideways problem. Many papers investigated the problem (with or without fractional derivatives) by using the Cauchy data measured at one interior point or using an interior data and the assumption . However, the flux is not easy to measure, and the temperature assumption at infinity is inappropriate for a bounded body. Hence, in the present paper, we consider a fractional sideways problem, in which the interior measurements at two interior point, namely, and , are given by continuous data with deterministic noises and by discrete data contaminated with random noises. We show that the problem is severely ill‐posed and further constructs an a posteriori optimal truncation regularization method for the deterministic data, and we also construct a nonparametric regularization for the discrete random data. Numerical examples show that the proposed method works well.

The freshwater discharge into the Adriatic Sea revisited

The present study reconstructs the river discharge climatology and its respective historical series for all rivers of the Adriatic Sea with averaged climatological daily river discharge above 1 m3s−1, to reach a better representation of the Adriatic rivers in hydrodynamic models and, consequently, to develop a more realistic freshwater balance in the different regions of the hydrographic basin. Based on the European Flood Awareness System (EFAS) data set, a careful method of identification and selection of the Adriatic rivers, followed by a rigorous assessment against observational data, was developed to evaluate the current state of the Adriatic river discharges and their respective trends throughout several climate indicators from 1991 to 2022. Observational data are limited to 85% of the identified rivers, totaling 98% of the overall freshwater input into the Adriatic Sea. The results confirm that the Shallow Northern Adriatic receives the largest freshwater inputs with a daily average exceeding 2,400 m3s−1, which amounts to 61% of the overall Adriatic discharges. Consequently, this region guides the freshwater seasonal cycle of the Adriatic Sea, which presents a well-defined pattern of two flood peaks in late autumn and late spring, separated by a minimum discharge period at mid-summer. From the Central to the Southern Adriatic subregions, the absence of snow-melting effects prevents the secondary flood peak during the spring, shaping the seasonal cycle of river discharges from a single flood peak in late autumn to a drought period in August. The 32 years of continuous river discharge data reveal a negligible trend in the overall Adriatic Sea but a negative trend for the last decade (2013–2022). This decadal decrease is driven by the extreme drought that drastically pounded the northern Adriatic in 2022.

Measurements of dune erosion processes during the RealDune/REFLEX experiments

Nearshore hydro- and morphodynamic data were collected during a field experiment under calm conditions, moderate conditions, and storm conditions with dune erosion in the collision regime. The experiment was conducted on the Sand Engine near Kijkduin, the Netherlands, from October 18, 2021, to January 7, 2022. Two artificial unvegetated dunes were constructed just above the high water line to measure storm erosion and dune impacts from higher water levels and waves. During the experiment, three storms occurred that resulted in significant erosion of both dunes. The collected hydrodynamic data include pressure sensor and velocimeter data along two cross-shore transects. The collected morphodynamic data include bathymetry and topography surveys, optical backscatter sensor data in the inner surf zone, and a continuous cross-shore line-scanning lidar data set of the dune face. This comprehensive data set can be used to (1) study relevant nearshore hydrodynamic and morphodynamic processes that occur during calm conditions, moderate conditions, and storm conditions with dune erosion in the collision regime, and (2) validate existing dune erosion models.

Efficacy of collagenase Clostridium histolyticum combination therapies for Peyronie's disease: a systematic review and meta-analysis.

Peyronie's disease (PD) is a common penile disorder characterized by the formation of fibrous noncompliant hard nodules in the tunica albuginea of the penis. Collagenase Clostridium histolyticum (CCH) is an injectable drug that treats PD by enzymatically degrading plaque interstitial collagen. CCH has been used in patients with varying curvature, as well as in the acute and stable phases of the disease, through a variety of treatment regimens and combinations. We carried out a systematic review and meta-analysis to assess the efficacy of CCH combination therapies for PD. We selected 4 observational comparative studies and 3 randomized controlled trials including 532 participants from the PubMed, Embase, and Cochrane databases (until December 2023) to evaluate the efficacy of CCH combination therapies for PD. The primary outcome was clinical efficacy as evaluated by improvement in penile curvature and penile length, as well as by scores on the Peyronie's Disease Questionnaire (PDQ) for symptom bother, penile pain, and psychological symptoms. Continuous data were represented by mean difference (MD) and 95% CI. All data were analyzed by Review Manager version 5.3. For penile length (MD,0.81cm; 95% CI, 0.17-1.45; P = .01), PDQ symptom bother (MD,-1.02; 95% CI, -1.83 to -0.21; P = .01), and PDQ penile pain (MD,-0.93; 95% CI, -1.50 to -0.36; P = .001), CCH combination therapy showed significantly greater improvements vs CCH monotherapy. However, in the other indicators, penile curvature and PDQ psychological symptoms, there was no significant difference between the therapies. This meta-analysis supports that CCH combination therapies can partially increase penile length and ameliorate symptom bother and penile pain to some extent. However, CCH combination therapies still need to be evaluated through more high-quality research.

Optimizing type 2 diabetes management: AI-enhanced time series analysis of continuous glucose monitoring data for personalized dietary intervention.

Despite advanced health facilities in many developed countries, diabetic patients face multifold health challenges. Type 2 diabetes mellitus (T2DM) go along with conspicuous symptoms due to frequent peaks, hypoglycemia <=70 mg/dL (while fasting), or hyperglycemia >=180 mg/dL two hours postprandial, according to the American Diabetes Association (ADA)). The worse effects of Type 2 diabetes mellitus are precisely associated with the poor lifestyle adopted by patients. In particular, a healthy diet and nutritious food are the key to success for such patients. This study was done to help T2DM patients improve their health by developing a favorable lifestyle under an AI-assisted Continuous glucose monitoring (CGM) digital system. This study aims to reduce the blood glucose level fluctuations of such patients by rectifying their daily diet and maintaining their exertion vs. food consumption records. In this study, a well-precise prediction is obtained by training the ML model on a dataset recorded from CGM sensor devices attached to T2DM patients under observation. As the data obtained from the CGM sensor is time series, to predict blood glucose levels, the time series analysis and forecasting are done with XGBoost, SARIMA, and Prophet. The results of different Models are then compared based on performance metrics. This helped in monitoring various trends, specifically irregular patterns of the patient's glucose data, collected by the CGM sensor. Later, keeping track of these trends and seasonality, the diet is adjusted accordingly by adding or removing particular food and keeping track of its nutrients with the intervention of a commercially available all-in-one AI solution for food recognition. This created an interactive assistive system, where the predicted results are compared to food contents to bring the blood glucose levels within the normal range for maintaining a healthy lifestyle and to alert about blood glucose fluctuations before the time that are going to occur sooner. This study will help T2DM patients get in managing diabetes and ultimately bring HbA1c within the normal range (<= 5.7%) for diabetic and pre-diabetic patients, three months after the intervention.

Work Productivity Impairment in Persons with Inflammatory Bowel Diseases: A Systematic Review and Meta-analysis.

The impact of inflammatory bowel disease (IBD) on work productivity remains unclear. In this systematic review and meta-analysis, we quantify work-related outcomes and employment data among persons with IBD. A systematic literature search was conducted in MEDLINE, EMBASE, the Cochrane library, Scopus, ProQuest, and clinicaltrials.gov from inception to February 2023 to identify studies on work productivity in persons with IBD aged >18 years. Work productivity was defined primarily by the Work Productivity and Activity Impairment (WPAI) questionnaire which includes absenteeism, presenteeism, overall work impairment, and non-work activity impairment. In addition, we included data on employment, sick leaves, disability pensions, and indirect costs due to productivity loss. Pooled effect analysis was conducted using a random-effects model for pooled estimates of continuous and proportional data with 95% confidence intervals. Among all patients with IBD, the pooled estimates were 16.4% for absenteeism, 35.9% for presenteeism, 39.4% for overall work impairment, and 46.0% for non-work activity impairment. Indirect costs from overall work impairment were 5,131.09 euros/patient/year. Only two thirds of IBD patients were employed and 1 in 3 patients lost their jobs due to IBD. Among those employed, 39.5% report sick days, 21.3% report work disability, and 12.3% receive disability pensions. Most studies demonstrate clinically meaningful improvements in work productivity with medical and/or surgical therapies. Persons with IBD experience significant work impairment and associated indirect costs. This highlights the need for appropriate workplace accommodations and timely medical therapy to alleviate the burden of disease and improve work outcomes.

A new family of distributions using a trigonometric function: Properties and applications in the healthcare sector

Probability distributions play a pivotal and significant role in modeling real-life data in every field. For this activity, a series of probability distributions have been introduced and exercised in applied sectors. This paper also contributes a new method for modeling continuous data sets. The proposed family is called the exponent power sine-G family of distributions. Based on the exponent power sine-G method, a new model, namely, the exponent power sine-Weibull model is studied. Several mathematical properties such as quantile function, identifiability property, and rth moment are derived. For the exponent power sine-G method, the maximum likelihood estimators are obtained. Simulation studies are also presented. Finally, the optimality of the exponent power sine-Weibull model is shown by taking two applications from the healthcare sector. Based on seven evaluating criteria, it is demonstrated that the proposed model is the best competing distribution for analyzing healthcare phenomena.

Classification of Diabetes Mellitus Sufferers Eating Patterns Using K-Nearest Neighbors, Naïve Bayes and Decission Tree

The study investigates three classification algorithms, namely K-Nearest Neighbor (K-NN), Naïve Bayes, and Decision Tree, for the classification of Diabetes Mellitus using a dataset from Kaggle. K-NN relies on distance calculations between test and training data, using the Euclidean distance formula. The choice of k, representing the nearest neighbor, significantly influences K-NN's effectiveness. Naïve Bayes, a probabilistic method, predicts class probabilities based on past events, and it employs the Gaussian distribution method for continuous data. Decision Trees, form prediction models with easily implementable rules. Data collection involves obtaining a Diabetes Mellitus dataset with eight attributes. Data preprocessing includes cleaning and normalization to minimize inconsistencies and incomplete data. The classification algorithms are applied using the Rapidminer tool, and the results are compared for accuracy. Naïve Bayes yields 77.34% accuracy, K-NN performance depends on the chosen k value, and Decision Trees generate rules for classification. The study provides insights into the strengths and weaknesses of each algorithm for diabetes classification