Long-term child follow-up after randomised controlled trials evaluating prevention of preterm birth interventions: A systematic review.

BACKGROUND: Obstetric interventions can have unexpected long-term effects on child development. However, evaluation of these outcomes is not standard in current research. OBJECTIVES: To give an overview of long-term follow-up studies after randomised controlled trials (RCTs) evaluating interventions to prevent preterm birth in asymptomatic pregnancies. SEARCH STRATEGY: Databases MEDLINE, Embase and Cochrane Central Library were searched for relevant RCTs. WebofScience was crosschecked for citations of these RCTs for publications on long-term child outcomes. SELECTION CRITERIA: RCTs evaluating interventions to prevent preterm birth in asymptomatic pregnant women. A long-term follow-up study was defined as such when child outcomes were assessed after discharge from hospital after birth. DATA COLLECTION AND ANALYSIS: Proportion of RCTs with long-term child follow-up was calculated with 95% confidence intervals (CI). Data on child's age at follow-up, outcome domains evaluated and measurement instruments were extracted. MAIN RESULTS: We included 188 RCTs that evaluated interventions to prevent preterm birth. Only 15 of these RCTs assessed long-term child outcomes (8.0%; 95% CI 4.5 to 12.8) with 17 publications (two RCTs performed two follow-up studies). In 13 out of 17 (76.5%) follow-up publications, neurodevelopment was the primary outcome domain studied for which seven different standardized measurement tools were used. In total, 14 different standardized measurement tools were used to assess long-term outcomes. Overall conclusion regarding the effectiveness of the intervention changed in six studies (6/14 studies; 42.9%) based on long-term follow-up findings compared to the original RCT. CONCLUSION: Only a minority of the RCTs evaluating prevention of preterm birth interventions performed a long-term follow-up study to assess long-term outcomes on child development. However, the overall conclusions changed in one third of cases. Performance of follow-up studies should be standard in this population in order to counsel parents on potential childhood effects. FUNDING: Amsterdam Reproduction and Development.

Bridging the gap: Insights in the immunopathology of Lyme borreliosis.

Lyme borreliosis (LB), caused by Borrelia burgdorferi sensu lato (Bbsl) genospecies transmitted by Ixodes spp. ticks, is a significant public health concern in the Northern Hemisphere. This review highlights the complex interplay between Bbsl infection and host-immune responses, impacting clinical manifestations and long-term immunity. Early localized disease is characterized by erythema migrans (EM), driven by T-helper 1 (Th1) responses and proinflammatory cytokines. Dissemination to the heart and CNS can lead to Lyme carditis and neuroborreliosis respectively, orchestrated by immune cell infiltration and chemokine dysregulation. More chronic manifestations, including acrodermatitis chronica atrophicans and Lyme arthritis, involve prolonged inflammation as well as the development of autoimmunity. In addition, dysregulated immune responses impair long-term immunity, with compromised B-cell memory and antibody responses. Experimental models and clinical studies underscore the role of Th1/Th2 balance, B-cell dysfunction, and autoimmunity in LB pathogenesis. Moreover, LB-associated autoimmunity parallels mechanisms observed in other infectious and autoimmune diseases. Understanding immune dysregulation in LB provides insights into disease heterogeneity and could provide new strategies for diagnosis and treatment.

Patient outcomes following buprenorphine treatment for opioid use disorder: A retrospective analysis of the influence of patient- and prescriber-level characteristics in Massachusetts, USA.

BACKGROUND AND AIMS: Opioid use disorder (OUD) is treatable with buprenorphine/naloxone (buprenorphine), but many patients discontinue treatment prematurely. The aim of this study was to assess the influence of patient- and prescriber-level characteristics relative to several patient outcomes following the initiation of buprenorphine treatment for OUD. DESIGN: This was a retrospective observational investigation. We used the Public Health Data Warehouse from the Massachusetts Department of Public Health to construct a sample of patients who initiated buprenorphine treatment between 2015 and 2019. We attributed each patient to a prescriber based on information from prescription claims. We used multilevel models to assess the influence of patient- and prescriber-level characteristics on each outcome. SETTING: Massachusetts, USA. PARTICIPANTS: The study cohort comprised 37 955 unique patients and 2146 prescribers. Among patients, 64.6% were male, 52.6% were under the age of 35 and 82.2% were White, non-Hispanic. For insurance coverage, 72.1% had Medicaid. MEASUREMENTS: The outcome measures were poor medication continuity, treatment discontinuation and opioid overdose, all assessed within a 12-month follow-up period that began with a focal prescription for buprenorphine. Each patient had a single follow-up period. Poor medication continuity was defined as medication gaps totaling more than 7 days during the initial 180 days of buprenorphine treatment and treatment discontinuation was defined as having a medication gap for 2 consecutive months within the 12-month follow-up period. FINDINGS: The patient-level rates for poor medication continuity, treatment discontinuation and opioid overdose were 59.7% [95% confidence interval (CI) = 59.2-60.2], 57.4% (95% CI = 56.9-57.9) and 10.3% (95% CI = 10.0-10.6), respectively, with 1.1% (95% CI = 1.0-1.2) experiencing a fatal opioid overdose. At the patient level, after adjustment for covariates, adverse outcomes were associated with race/ethnicity as both Black, non-Hispanic and Hispanic patients had worse outcomes than did White, non-Hispanic patients (Black, non-Hispanic -- poor continuity: 1.50, 95% CI = 1.34-1.68; discontinuation: 1.44, 95% CI = 1.30-1.60; Hispanic -- poor continuity: 1.21, 95% CI = 1.12-1.31; discontinuation: 1.38, 95% CI = 1.28-1.48). Patients with insurance coverage through Medicaid also had worse outcomes than those with commercial insurance (poor continuity: 1.18, 95% CI = 1.11-1.26; discontinuation: 1.09, 95% CI = 1.03-1.16; overdose: 1.98, 95% CI = 1.75-2.23). Pre-treatment mental health conditions and other types of chronic illness were also associated with worse outcomes (History of mental health conditions -- poor continuity: 1.11, 95% CI = 1.06-1.17; discontinuation: 1.05, CI = 1.01-1.10; overdose: 1.47, 95% CI = 1.36-1.60; Chronic health conditions -- poor continuity: 1.15, 95% CI = 1.05-1.27; discontinuation: 1.15, 95% CI = 1.05-1.26; overdose: 1.83, 95% CI = 1.60-2.10; History of substance use disorder other than for opioids -- poor continuity: 1.54, 95% CI = 1.46-1.62; discontinuation: 1.54, 95% CI = 1.47-1.62; overdose: 1.93, 95% CI = 1.80-2.07). At the prescriber level, after adjustments for covariates, adverse outcomes were associated with clinical training, as primary care physicians had higher rates of adverse outcomes than psychiatrists (poor continuity: 1.12, 95% CI = 1.02-1.23; discontinuation: 1.04, 95% CI = 1.01-1.09). A larger prescriber panel size, based on number of patients being prescribed buprenorphine, was also associated with higher rates of adverse outcomes (poor continuity: 1.36, 95% CI = 1.27-1.46; discontinuation: 1.21, 95% CI = 1.14-1.28; overdose: 1.10, 95% CI = 1.01-1.19). Between 9% and 15% of the variation among patients for the outcomes was accounted for at the prescriber level. CONCLUSIONS: Patient- and prescriber-level characteristics appear to be associated with patient outcomes following buprenorphine treatment for opioid use disorder. In particular, patients' race/ethnicity and insurance coverage appear to be associated with substantial disparities in outcomes, and prescriber characteristics appear to be most closely associated with medication continuity during early treatment.

Barriers to T Cell Functionality in the Glioblastoma Microenvironment.

Glioblastoma (GBM) is an aggressive primary brain tumor depicted by a cold tumor microenvironment, low immunogenicity, and limited effective therapeutic interventions. Its location in the brain, a highly immune-selective organ, acts as a barrier, limiting immune access and promoting GBM dissemination, despite therapeutic interventions. Currently, chemotherapy and radiation combined with surgical resection are the standard of care for GBM treatment. Although immune checkpoint blockade has revolutionized the treatment of solid tumors, its observed success in extracranial tumors has not translated into a significant survival benefit for GBM patients. To develop effective immunotherapies for GBM, it is vital to tailor treatments to overcome the numerous immunosuppressive barriers that inhibit T cell responses to these tumors. In this review, we address the unique physical and immunological barriers that make GBM challenging to treat. Additionally, we explore potential therapeutic mechanisms, studied in central nervous system (CNS) and non-CNS cancers, that may overcome these barriers. Furthermore, we examine current and promising immunotherapy clinical trials and immunotherapeutic interventions for GBM. By highlighting the array of challenges T cell-based therapies face in GBM, we hope this review can guide investigators as they develop future immunotherapies for this highly aggressive malignancy.

Mitigating Sociodemographic Bias in Opioid Use Disorder Prediction: Fairness-Aware Machine Learning Framework.

BACKGROUND: Opioid use disorder (OUD) is a critical public health crisis in the United States, affecting >5.5 million Americans in 2021. Machine learning has been used to predict patient risk of incident OUD. However, little is known about the fairness and bias of these predictive models. OBJECTIVE: The aims of this study are two-fold: (1) to develop a machine learning bias mitigation algorithm for sociodemographic features and (2) to develop a fairness-aware weighted majority voting (WMV) classifier for OUD prediction. METHODS: We used the 2020 National Survey on Drug and Health data to develop a neural network (NN) model using stochastic gradient descent (SGD; NN-SGD) and an NN model using Adam (NN-Adam) optimizers and evaluated sociodemographic bias by comparing the area under the curve values. A bias mitigation algorithm, based on equality of odds, was implemented to minimize disparities in specificity and recall. Finally, a WMV classifier was developed for fairness-aware prediction of OUD. To further analyze bias detection and mitigation, we did a 1-N matching of OUD to non-OUD cases, controlling for socioeconomic variables, and evaluated the performance of the proposed bias mitigation algorithm and WMV classifier. RESULTS: Our bias mitigation algorithm substantially reduced bias with NN-SGD, by 21.66% for sex, 1.48% for race, and 21.04% for income, and with NN-Adam by 16.96% for sex, 8.87% for marital status, 8.45% for working condition, and 41.62% for race. The fairness-aware WMV classifier achieved a recall of 85.37% and 92.68% and an accuracy of 58.85% and 90.21% using NN-SGD and NN-Adam, respectively. The results after matching also indicated remarkable bias reduction with NN-SGD and NN-Adam, respectively, as follows: sex (0.14% vs 0.97%), marital status (12.95% vs 10.33%), working condition (14.79% vs 15.33%), race (60.13% vs 41.71%), and income (0.35% vs 2.21%). Moreover, the fairness-aware WMV classifier achieved high performance with a recall of 100% and 85.37% and an accuracy of 73.20% and 89.38% using NN-SGD and NN-Adam, respectively. CONCLUSIONS: The application of the proposed bias mitigation algorithm shows promise in reducing sociodemographic bias, with the WMV classifier confirming bias reduction and high performance in OUD prediction.

Self-help groups and opioid use disorder treatment: An investigation using a machine learning-assisted robust causal inference framework.

OBJECTIVES: This study investigates the impact of participation in self-help groups on treatment completion among individuals undergoing medication for opioid use disorder (MOUD) treatment. Given the suboptimal adherence and retention rates for MOUD, this research seeks to examine the association between treatment completion and patient-level factors. Specifically, we evaluated the causal relationship between self-help group participation and treatment completion for patients undergoing MOUD. METHODS: We used the Substance Abuse and Mental Health Services Administration's (SAMHSA) Treatment Episode Data Set: Discharges (TEDS-D) from 2015 to 2019. The data are filtered by the patient's opioid use history, demographics, treatment modality, and other relevant information. In this observational study, machine learning models (Lasso Regression, Decision Trees, Random Forest, and XGBoost) were developed to predict treatment completion. Outcome Adaptive Elastic Net (OAENet) was used to select confounders and outcome predictors, and the robust McNemars test was used to evaluate the causal relationship between self-help group participation and MOUD treatment completion. RESULTS: The machine-learning models showed a strong association between participation in self-help groups and treatment completion. Our causal analysis demonstrated an average treatment effect on treated (ATT) of 0.260 and a p-value < 0.0001 for the robust McNemars test. CONCLUSIONS: Our study demonstrates the importance of participation in self-help groups for MOUD treatment recipients. We found that participation in MOUD along with self-help groups caused higher chances of treatment completion than MOUD alone. This suggests that policymakers should consider further integrating self-help groups into the treatment for OUD to improve the adherence and completion rate.

An explainable machine learning framework for predicting the risk of buprenorphine treatment discontinuation for opioid use disorder among commercially insured individuals.

OBJECTIVES: Buprenorphine is an effective evidence-based medication for opioid use disorder (OUD). Yet premature discontinuation undermines treatment effectiveness, increasing the risk of mortality and overdose. We developed and evaluated a machine learning (ML) framework for predicting buprenorphine care discontinuity within 12 months following treatment initiation. METHODS: This retrospective study used United States (US) 2018-2021 MarketScan commercial claims data of insured individuals aged 18-64 who initiated buprenorphine between July 2018 and December 2020 with no buprenorphine prescriptions in the previous six months. We measured buprenorphine prescription discontinuation gaps of ≥30 days within 12 months of initiating treatment. We developed predictive models employing logistic regression, decision tree classifier, random forest, extreme gradient boosting, Adaboost, and random forest-extreme gradient boosting ensemble. We applied recursive feature elimination with cross-validation to reduce dimensionality and identify the most predictive features while maintaining model robustness. For model validation, we used several statistics to evaluate performance, such as C-statistics and precision-recall curves. We focused on two distinct treatment stages: at the time of treatment initiation and one and three months after treatment initiation. We employed SHapley Additive exPlanations (SHAP) analysis that helped us explain the contributions of different features in predicting buprenorphine discontinuation. We stratified patients into risk subgroups based on their predicted likelihood of treatment discontinuation, dividing them into decile subgroups. Additionally, we used a calibration plot to analyze the reliability of the models. RESULTS: A total of 30,373 patients initiated buprenorphine and 14.98% (4551) discontinued treatment. C-statistic varied between 0.56 and 0.76 for the first-stage models including patient-level demographic and clinical variables. Inclusion of proportion of days covered (PDC) measured after one month and three months following treatment initiation significantly increased the models' discriminative power (C-statistics: 0.60 to 0.82). Random forest (C-statistics: 0.76, 0.79 and 0.82 with baseline predictors, one-month PDC and three-months PDC, respectively) outperformed other ML models in discriminative performance in all stages (C-statistics: 0.56 to 0.77). Most influential risk factors of discontinuation included early stage medication adherence, age, and initial days of supply. CONCLUSION: ML algorithms demonstrated a good discriminative power in identifying patients at higher risk of buprenorphine care discontinuity. The proposed framework may help healthcare providers optimize treatment strategies and deliver targeted interventions to improve buprenorphine care continuity.

Removal of Carbon Dioxide and Hydrogen Sulfide from Natural Gas Using a Hybrid Solvent of Monoethanolamine and N-Methyl 2-Pyrrolidone.

The main goal of traditional methods for sweetening natural gas (NG) is to remove hydrogen sulfide (H2S) and significantly lower carbon dioxide (CO2). However, when NG processes are integrated into the carbon capture and storage (CCS) framework, there is potential for synergy between these two technologies. A steady-state model utilizing a hybrid solvent consisting of N-methyl-2-pyrrolidone (NMP) and monoethanolamine (MEA) has been developed to successfully anticipate the CO2 and H2S capture process from NG. The model was tested against important variables affecting process performance. This article specifically explores the impact of operational parameters such as lean amine temperature, absorber pressure, and amine flow rate on the concentrations of CO2 and H2S in the sweet gas and reboiler duty. The result shows that hybrid solvents (MEA + NMP) perform better in removing acid gases and reducing reboiler duty than conventional chemical solvent MEA. The primary purpose is to meet product requirements while consuming the least energy possible, which is in line with any process plant's efficiency goals.

A review on recent advances of cellulose acetate membranes for gas separation.

This review thoroughly investigates the wide-ranging applications of cellulose-based materials, with a particular focus on their utility in gas separation processes. By focusing on cellulose acetate (CA), the review underscores its cost-effectiveness, robust mechanical attributes, and noteworthy CO2 solubility, positioning it as a frontrunner among polymeric gas separation membranes. The synthesis techniques for CA membranes are meticulously examined, and the discourse extends to polymeric blend membranes, underscoring their distinct advantages in gas separation applications. The exploration of advancements in CA-based mixed matrix membranes, particularly the incorporation of nanomaterials, sheds light on the significant versatility and potential improvements offered by composite materials. Fabrication techniques demonstrate exceptional gas separation performance, with selectivity values reaching up to 70.9 for CO2/CH4 and 84.1 for CO2/N2. CA/PEG (polyethylene glycol) and CA/MOF (metal-organic frameworks) demonstrated exceptional selectivity in composite membranes with favorable permeability, surpassing other composite CA membranes. Their selectivity with good permeability lies well above all the synthesised cellulose. As challenges in experimental scale separation emerge, the review seamlessly transitions to molecular simulations, emphasizing their crucial role in understanding molecular interactions and overcoming scalability issues. The significance of the review lies in addressing environmental concerns, optimizing membrane compositions, understanding molecular interactions, and bridging knowledge gaps, offering guidance for the sustainable evolution of CA-based materials in gas separation technologies.

Comparative Study of Treatment Outcome with Tenofovir Alafenamide and Entecavir in Patients with HBV Related Acute on Chronic Liver Failure.

Major causes of acute insult in Hepatitis B virus related acute on chronic liver failure in the Asian region are reactivation of Hepatitis B virus and super infection with hepatitis A and E virus (ACLF). Anti viral therapy should be started as soon as possible in the ACLF patients at presentation while waiting for confirmation by HBV DNA level. This randomized controlled trial was carried out at the Department of Hepatology, BSMMU, Bangladesh from September 2019 to august 2020 with Hepatitis B virus related ACLF patient. This trial was conducted among twenty seven HBV acute on chronic liver failure patient to compare Child Turcotte pugh (CTP) score, Model for end stage liver disease (MELD) score, Asia Pacific Association for study of Liver (APASL) ACLF Research consortium (AARC) score, survival of the patients and HBV DNA level at 3 months with antiviral therapy between tenofovir alafenamide (25mg) and entecavir (0.5mg) group. CTP score, MELD score and AARC score were significantly (p<0.05) decline from baseline to all subsequent follow-up at 1st (at 7 days), 2nd (at 14 days), 3rd (at 30 days) and 4th (at 90 days) in each group but non significant (p>0.05) difference occurred between two group. All twenty seven patients had detectable HBV DNA level at pre-treatment and all survived patients became undectable at 4th, 90 days follow-up. Total 10 patients (37.07%) were survived at 90 days follow-up, out of them seven patients (70.0%) were in tenofovir alafenamide group and three patients (30.0%) were in entecavir group which was statistically significant (p<0.05) in between two group. Hepatic encephalopathy and hepatorenal syndrome were most common causes of death in both groups. Both drugs tenofovir alafenamide and entecavir significantly improves liver functions but the former one is superior regarding survival.

Staging and Management of Cervical Cancer at the Colposcopy Clinic of Bangabandhu Sheikh Sheikh Mujib Medical University (BSMMU), Bangladesh.

Anjuman SultanaCervical cancer (CC) ranks as the second most common cancer among women in Bangladesh. Unfortunately, due to late-stage diagnosis and inadequate treatment facilities, the mortality rate remains high. The stage at which CC is diagnosed plays a crucial role in predicting a woman's survival. This study aimed to determine the staging patterns of CC at presentation in the colposcopy clinic of Bangabandhu Sheikh Mujib Medical University (BSMMU) and the subsequent management provided to these women. This retrospective study was conducted at the BSMMU colposcopy clinic from January 2019 to June 2020. It included CC cases with known staging, spanning from January 2016 to June 2019. Data were collected from the colposcopy registry book, telephone interviews, and face-to-face discussions during follow-up appointments. The mean age of women diagnosed with CC was 48.59 years (±2 standard deviations). Among the 523 women studied, 107 (20.5%) were diagnosed at stage I, 124 (23.7%) at stage IIA, 240 (45.9%) at stage IIB, while the remaining 52 (9.90%) were diagnosed with Stage III and IV disease. Within the study population, 39 (7.5%) women underwent radical hysterectomy alone, 110 (21.00%) underwent radical hysterectomy followed by adjuvant therapy, and 184 (35.14%) received primary radiotherapy or concurrent chemoradiation. A significant portion of CC cases presented at an early stage, underscoring the importance of an effective screening program to prevent advanced-stage disease and enhance early detection rates. Establishing a patient navigation system immediately after diagnosis is crucial to prevent the loss of follow-up and ensure timely treatment. It is imperative to enhance the healthcare system's capacity to ensure timely treatment for cancer patients.

The Modulation of Euglycemic Endocrine and Exocrine Pancreatic Secretions in Iron Deficiency.

OBJECTIVES: The contribution of pancreatic secretions in iron metabolism has been elucidated, but the clinical outcomes of iron deficiency on pancreatic function are debatable. This study aimed to investigate the modulation of euglycemic endocrine and exocrine pancreatic excretions in response to variations in iron availability. SUBJECTS AND METHODS: Serum levels of insulin, glucagon, insulin-to-glucagon ratio (IGR), and amylase were determined in 170 adult subjects with variable levels of serum iron. RESULTS: Control (n = 46) and iron-deficient (n = 124) subjects had significant differences (p < 0.001) in their average levels of insulin (68.7 ± 0.5 vs. 100.0 ± 2.0 pmol/dL), glucagon (17.9 ± 0.6 vs. 10.8 ± 0.8 pmol/dL), IGR (4.0 ± 0.1 vs. 19.5 ± 2.1), and amylase (29.7 ± 0.9 vs. 17.5 ± 0.2). The upregulation of serum insulin levels increases proportionally and gradually to the extent of iron deficiency as compared to an abrupt downregulation of serum levels of glucagon and amylase. A significant association was observed between serum iron and IGR (r = -0.645, p < 0.001) and amylase levels (r = 0.653, p < 0.001). The receiver operating characteristic curve analysis defines an excellent predictivity of the reduced serum iron level to discriminate subjects with upregulated IGR and amylase levels with area under curves of 0.938 and 0.905, respectively. CONCLUSION: Iron deficiency is associated with an adaptive modulation of euglycemic endocrine and exocrine secretions that is consistent with a status of insulin resistance.

Assessment of heavy metals accumulation by vegetables irrigated with different stages of textile wastewater for evaluation of food and health risk.

Wastewater irrigation for vegetable cultivation is greatly concerned about the presence of toxic metals in irrigated soil and vegetables which causes possible threats to human health. This study aimed to ascertain the accumulation of heavy metals (HMs) in edible parts of vegetables irrigated with different stages of textile dyeing wastewater (TDW). Bio-concentration factor (BCF), Estimated daily intake (EDI), and target hazard quotient (THQ) were computed to estimate human health risks and speculate the hazard index (HI) of adults and children with the consumption of HMs contaminated vegetables at recommended doses. Five vegetables (red amaranth, Indian spinach, cauliflower, tomato, and radish) in a pot experiment were irrigated with groundwater (T1) and seven stages of TDW (T2∼T8) following a randomized complete block design (RCBD) with three replications. Among the TDW stages, T8, T7, T4, and T5 exhibited elevated BCF, EDI, THQ, and HI due to a rising trend in the accumulation of Pb, Cd, Cr, and Ni heavy metals in the edible portion of the red amaranth, followed by radish, Indian spinach, cauliflower, and tomato. The general patterns of heavy metal (HM) accumulation, regarded as vital nutrients for plants, were detected in the following sequence: Zn > Mn/Cu > Fe. Conversely, toxic metals were found to be Cd/Cr > Ni > Pb, regardless of the type of vegetables. Principal Component Analysis (PCA) identified T8, T7, and T4 of TDW as the primary contributors to the accumulation of heavy metals in the vegetables examined. Furthermore, the analysis of the heavy metals revealed that the BCF, THQ, and HI values for all studied metals were below 1, except for Pb. This suggests that the present consumption rates of different leafy and non-leafy vegetables, whether consumed individually or together, provide a low risk in terms of heavy metal exposure. Nevertheless, the consumption of T8, T7, and T4 irrigated vegetables, specifically Indian spinach alone or in combination with red amaranth and radish, by both adults and children, at the recommended rate, was found to pose potential health risks. On the other hand, T2, T3, and T6 irrigated vegetables were deemed safe for consumption. These findings indicated that the practice of irrigating the vegetables with T8, T7, and T4 stages of TDW has resulted in a significant buildup of heavy metals in the soils and edible parts of vegetables which are posing health risks to adults and children. Hence, it is imperative to discharge the T8, T7, and T4 stages of TDW after ETP to prevent the contamination of vegetables and mitigate potential health risks.

Colour, nutritional composition and antioxidant properties of dehydrated carrot (Daucus carota var. sativus) using solar drying techniques and pretreatments.

Carrot is a seasonal perishable tuberous root vegetable which presents a preservation challenge owing to its elevated moisture content. Recently, carrot processing has received more attention because of its many health-promoting qualities and the reduction of postharvest losses in a cost-effective safe way. This study was designed to sort out the effective solar drying technique including pre-treatment that would retain the color and quality characteristics of dehydrated carrot. Carrot slices were subjected to dry using open sun drying (D1), solar drying long chimney (D2), solar drying short chimney (D3) and box solar drying (D4) techniques with the pretreatments of ascorbic acid 1 % (C3), citric acid 5 % (C4), potassium metabisulfite 1 % (C5) and potassium sodium tartrate 0.3 % (C6) before drying. Drying characteristics, nutritional attributes, phytochemicals and antioxidant of the dehydrated carrot samples were compared with the fresh sample and untreated (control) sample. Results showed that D4 was a good drying method to preserve nutritional quality with good appearance. Among the pretreatments, C5 and C4 resulted improved nutritional quality retention, enhanced visual acceptability and enriched antioxidant activities. PCA (Principal Component Analysis) and correlation matrix revealed that D4 with C5 retained the maximum amount of vitamin, minerals, total phenolic content, antioxidant and admirable dehydrated carrot color by inactivating enzymatic reaction. Therefore, box solar drying with potassium metabisulfite pretreatment would be very promising for functional carrot drying retaining acceptable color and nutrition composition.

Hippocampal cognitive and relational map paradigms explored by multisensory encoding recording with wide-field calcium imaging.

Two major theories have been proposed to explain hippocampal function: cognitive map and the relational theories. They differ in their views on whether hippocampal neurons can process non-spatial information independently. However, the explanatory power of these theories remains unresolved. Additionally, more complex aspects of hippocampal neural population responses to non-spatial stimuli have not been investigated. Here, we used miniaturized fluorescence microscopy to investigate mouse CA1 responses to spatial, visual, auditory modalities, and combinations. We found that while neuronal populations primarily processed spatial information, they also showed strong sensitivity to non-spatial modalities independent of spatial inputs, exhibiting distinct neuronal dynamics and coding patterns. These results provide strong support for the relational theories.

Screening of Metal Oxides and Hydroxides for Arsenic Removal from Water Using Molecular Dynamics Simulations.

Arsenic in groundwater is a harmful and hazardous substance that must be removed to protect human health and safety. Adsorption, particularly using metal oxides, is a cost-effective way to treat contaminated water. These metal oxides must be selected systematically to identify the best material and optimal operating conditions for the removal of arsenic from water. Experimental research has been the primary emphasis of prior work, which is time-consuming and costly. The previous simulation studies have been limited to specific adsorbents such as iron oxides. It is necessary to study other metal oxides to determine which ones are the most effective at removing arsenic from water. In this work, a molecular simulation computational framework using molecular dynamics and Monte Carlo simulations was developed to investigate the adsorption of arsenic using various potential metal oxides. The molecular structures have been optimized and proceeded with sorption calculations to observe the adsorption capabilities of metal oxides. In this study, 15 selected metal oxides were screened at a pressure of 100 kPa and a temperature of 298 K for As(V) in the form of HAsO4 at pH 7. Based on adsorption capacity calculations for selected metal oxides/hydroxides, aluminum hydroxide (Al(OH)3), ferric hydroxide (FeOOH), lanthanum hydroxide La(OH)3, and stannic oxide (SnO2) were the most effective adsorbents with adsorption capacities of 197, 73.6, 151, and 42.7 mg/g, respectively, suggesting that metal hydroxides are more effective in treating arsenic-contaminated water than metal oxides. The computational results were comparable with previously published literature with a percentage error of 1%. Additionally, SnO2, which is rather unconventional to be used in this application, demonstrates potential for arsenic removal and could be further explored. The effects of pH from 1 to 13, temperature from 281.15 to 331.15 K, and pressure from 100 to 350 kPa were studied. Results revealed that adsorption capacity decreased for the high-temperature applications while experiencing an increase in pressure-promoted adsorption. Furthermore, response surface methodology (RSM) has been employed to develop a regression model to describe the effect of operating variables on the adsorption capacity of screened adsorbents for arsenic removal. The RSM models utilizing CCD (central composite design) were developed for Al(OH)3, La(OH)3, and FeOOH, having R2 values 0.92, 0.67, and 0.95, respectively, suggesting that the models developed were correct.

Postoperative wound infection by nontuberculous mycobacteria; case series in Dhaka Medical College Hospital of Bangladesh.

The incidence of nontuberculous mycobacterial (NTM) infections after operations is increasing in Bangladesh but data regarding clinical presentation, diagnosis, treatment, and prognosis after treatment are lacking. In this case series, three patients having persistent serous discharge from incision wound after operation were studied. Discharge from wounds were collected, wet film microscopy was performed for pus cells and fungus, Gram stain, Ziehl-Neelsen (ZN) stain, culture in routine culture media and Lowenstein-Jensen (LJ) media, Xene-Xpert for mycobacterium tuberculosis (MTB), polymerase chain reaction (PCR) for NTM were done. NTM-positive patients were treated initially for 6 weeks with four drugs regimen (clarithromycin 500 mg 12 hourly, ciprofloxacin 500 mg 12 hourly, linezolid 400 mg 12 hourly, and amikacin 500 mg 12 hourly), followed by 5 months with three drugs regimen (clarithromycin 500 mg 12 hourly, ciprofloxacin 500 mg 12 hourly, and linezolid 400 mg 12 hourly) as a maintenance dose. Cessation of discharge occurred within 3-4 weeks after starting treatment, and the wounds were healed.

Work productivity in patients with axial spondyloarthritis initiating biological or targeted synthetic disease-modifying antirheumatic drugs: a systematic literature review and meta-analysis.

BACKGROUND: Axial spondyloarthritis (axSpA) can limit work participation. Our objective was to characterise productivity in patients with axSpA, including changes after 12-16 weeks of treatment with biological and targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs). METHODS: A systematic literature review identified studies published from 1 January 2010 to 21 October 2021 reporting work productivity using the Work Productivity and Activity Impairment (WPAI) questionnaire in patients with axSpA initiating b/tsDMARDs. Baseline and Week 12-16 overall work productivity, absenteeism, presenteeism and activity impairment scores were used in a random-effects meta-analysis to calculate absolute mean change from baseline for each WPAI-domain. RESULTS: Eleven studies in patients with axSpA who received either placebo (n=727) or treatment with adalimumab, bimekizumab, etanercept, ixekizumab, secukinumab or tofacitinib (n=994) were included. In working patients initiating a b/tsDMARD, mean baseline overall work productivity impairment, absenteeism and presenteeism scores were 52.1% (N=7 studies), 11.0% and 48.8% (N=6 studies), respectively. At Week 12-16, the pooled mean change from baseline in overall work impairment for b/tsDMARDs or placebo was -21.6% and -12.3%. When results were extrapolated to 1 year, the potential annual reductions in cost of paid and unpaid productivity loss per patient ranged from €11 962.88 to €14 293.54. CONCLUSIONS: Over 50% of employed patients with active axSpA experienced work impairment, primarily due to presenteeism. Overall work productivity improved at Weeks 12-16 to a greater extent for patients who received b/tsDMARDs than placebo. Work productivity loss was associated with a substantial cost burden, which was reduced with improvements in impairment.