Temporal and differential proteomic profile of molecular mediators associated with chronic and acute wound healing.

The underlying pathophysiology of nonhealing chronic wounds is poorly understood due to the changes occurring at the gene level and the complexity arising in their proteomic profile. Here, we elucidated the temporal and differential profile of the normal and diabetic wound-healing mediators along with their interactions and associated pathways. Skin tissues corresponding to normal and diabetic wounds were isolated at Days 0, 3, 6, and 9 representing different healing phases. Temporal gene expression was analyzed by quantitative real-time PCR. Concurrently, differential protein patterns in the wound tissues were identified by Nano LC-ESI-TOF mass spectrometry and later confirmed by Western blot analysis. Gene ontology annotation, protein-protein interaction, and protein pathway analysis were performed using DAVID, PANTHER, and STRING bioinformatics resources. Uniquely identified proteins (complement C3, amyloid beta precursor protein, and cytoplasmic linker associated protein 2) in the diabetic wound tissue implied that these proteins are involved in the pathogenesis of diabetic wound. They exhibit enhanced catalytic activity, trigger pathways linked with inflammation, and negatively regulate wound healing. However, in the normal wound tissue, axin 1, chondroitin sulfate proteoglycan 4, and sphingosine-1-phosphate receptor were identified, which are involved in proliferation, angiogenesis, and remodeling. Our findings demonstrate the correlation between elevated gene expression of tumor necrosis factor-α, interleukin (IL)-1ß, and identified mediators: aryl hydrocarbon receptor nuclear translocator, 5'-aminolevulinate synthase 2, and CXC-family, that inflicted an inflammatory response by activating downstream MAPK, JAK-STAT, and NF-κB pathways. Similarly, in normal wound tissue, the upregulated IL-4 and hepatocyte growth factor levels in conjunction with the identified proteins, serine/threonine-protein kinase mTOR and peroxisome proliferator-activated receptor gamma, played a significant role in the cellular response to platelet-derived growth factor stimulus, dermal epithelialization, and cell proliferation, processes associated with the repair mechanism. Furthermore, Western blot analysis indicated elevated levels of inflammatory markers and reduced levels of proliferative and angiogenic factors in the diabetic wound.

A copula-based method of risk prediction for autonomous underwater gliders in dynamic environments.

Autonomous underwater gliders (AUGs) are effective platforms for oceanic research and environmental monitoring. However, complex underwater environments with uncertainties could pose the risk of vehicle loss during their missions. It is therefore essential to conduct risk prediction to assist decision making for safer operations. The main limitation of current studies for AUGs is the lack of a tailored method for risk analysis considering both dynamic environments and potential functional failures of the vehicle. Hence, this study proposed a copula-based approach for evaluating the risk of AUG loss in dynamic underwater environments. The developed copula Bayesian network (CBN) integrated copula functions into a traditional Bayesian belief network (BBN), aiming to handle nonlinear dependencies among environmental variables and inherent technical failures. Specifically, potential risk factors with causal effects were captured using the BBN. A Gaussian copula was then employed to measure correlated dependencies among identified risk factors. Furthermore, the dependence analysis and CBN inference were performed to assess the risk level of vehicle loss given various environmental observations. The effectiveness of the proposed method was demonstrated in a case study, which considered deploying a Slocum G1 Glider in a real water region. Risk mitigation measures were provided based on key findings. This study potentially contributes a tailored tool of risk prediction for AUGs in dynamic environments, which can enhance the safety performance of AUGs and assist in risk mitigation for decision makers.

Mortality in Qatari individuals with mental illness: a retrospective cohort study.

INTRODUCTION: There is substantial evidence that people with mental illness have higher mortality rates than the general population. However, most of the studies were from Western countries, and it is not clear whether this finding also applies to Arab countries like Qatar. OBJECTIVES: We aimed to explore whether mortality in patients with mental illness in Qatar, is different from those without. METHODS: We conducted a retrospective cohort study, including all Qatari nationals deceased in 2017 and 2018, using the list of registered deaths from Hamad Medical Corporation (HMC) Mortuary. We divided the cohort of deceased people into two groups: with and without mental illness. For each of the groups, we collected the age at death, the reported cause of death as well as sociodemographic and clinical data. RESULTS: There were 602 registered deaths in 2017 and 589 deaths in 2018. The prevalence of mental illness was 20.4%. Compared to subjects without mental illness, subjects with mental illness surprisingly had higher age at death (median ± IQR = 76.5 ± 22.1 years vs. 62.7 ± 32.9 years; p < .001). This difference persisted even after we controlled for covariates. Individuals with mental illness were more likely to die of an infection (OR = 1.98[1.44;2.71]), or of chronic respiratory disease (OR = 3.53 [1.66;7.52]) but less likely to die because of accidental (OR = 0.21[0.09;0.49]) or congenital causes (OR = 0.18[0.04;0.77]). CONCLUSION: Contrary to most previous studies, we did not find that mortality was higher in Qatari individuals with mental illness. Sociocultural factors, free and easy-to-access healthcare, and an enhanced role of mental health professionals in detecting medical comorbidities may explain this finding.

Neurofibroma: Case Series with Clinical Features and Recommendations.

Neurofibroma is an autosomal benign disorder. It can be localized, diffuse or invasive like plexiform neurofibroma that involves the nerves, muscle, tissues, skeleton. It represents itself as a destructive variant of neurofibroma, mostly present as orbital or periorbital neurofibroma or may be associated with autosomal dominant disease. Clinical diagnosis of neurofibromatosis (NF) according to National Institutes of Health (NIH) criteria should have more than two of the seven features including lisch nodules, cafe'- au-lait spots, plexiform neurofibroma, optic glioma, freckling, first degree relative with NF or dysplasia of cortical bones. However, proper early diagnosis is still crucial due to its various presentation such as cheek mass, painless swelling on skin, chalazion, intratracheal tumor, genital swelling or ptosis. It is reported that neurofibroma often represents as ocular or facial swelling. Here we are presenting features of neurofibroma of eight cases of patients from Civil Hospital, Karachi. These cases had main complain of overhanging skin mass mainly on orbital or periorbital region that damage the area and with poor daily activities. Multiple nodules on face and body along with them Cafe'-au-lait spots and lisch nodules were main signs. While, other signs i.e. ptosis, pterygium, telecanthus and muddy discoloration of conjunctiva need further evaluation for correlation with neurofibromatosis. Debulking surgery was planned for most of the cases but the huge disfigurement caused by overhanging skin mass and nodules made it a challenge for plastic surgeons to provide good outcomes with minimum damage. Keywords: neurofibroma; lisch nodules; ptosis; Cafe'-au-lait spot; periorbital; overhanging skin.

Convolutional-neural-network-based versus vision-transformer-based SNR estimation for visible light communication networks.

Visible light communication (VLC) has emerged as a promising technology for future sixth-generation (6 G) communications. Estimating and predicting the impairments, such as turbulence and free space signal scattering, can help to construct flexible and adaptive VLC networks. However, the monitoring of impairments of VLC is still in its infancy. In this Letter, we experimentally demonstrate a deep-neural-network-based signal-to-noise ratio (SNR) estimation scheme for VLC networks. A vision transformer (ViT) is first utilized and compared with the conventional scheme based on a convolutional neural network (CNN). Experimental results show that the ViT-based scheme exhibits robust performance in SNR estimation for VLC networks compared to the CNN-based scheme. Specifically, the ViT-based scheme can achieve accuracies of 76%, 63.33%, 45.33%, and 37.67% for 2-quadrature amplitude modulation (2QAM), 4QAM, 8QAM, and 16QAM, respectively, against 65%, 57.67%, 41.67%, and 34.33% for the CNN-based scheme. Additionally, data augmentation has been employed for achieving enhanced SNR estimation accuracies of 95%, 79.67%, 58.33%, and 50.33% for 2QAM, 4QAM, 8QAM, and 16QAM, respectively. The effect of the SNR step size of a contour stellar image dataset on the SNR estimation accuracy is also studied.

Low incidence of de novo HLA antibodies after COVID-19 vaccination: A cohort study of patients awaiting kidney transplantation.

BACKGROUND: Antibodies against human leukocyte antigen (anti-HLA Abs) are associated with an increased risk of allograft loss. Herein, we report the prospective follow-up for anti-HLA Abs formation in 103 patients with end-stage kidney disease on the waiting list for transplantation who underwent COVID-19 vaccination. PATIENTS AND METHODS: Sera were tested before and after vaccination using Luminex technology. The cohort comprised of 62 males and 41 females with a mean age of 56 ± 14 years. The patients received BNT162b2 (80.4%), mRNA-1273 (18.5%), AZD1222 (0.40%), or ChAdOx1-S (0.80%) vaccine. Patients were tested before and within 119 ± 50, 95 ± 46 and 25 ± 26 days after the first, second, and third dose of the vaccine, respectively. RESULTS: No significant change in calculated panel reactive antibody (cPRA) after vaccination was seen. Although 98.1% of patients had no change in anti-HLA Abs profile or cPRA after vaccination, two patients (1.9%) developed de novo anti-HLA Abs against class I or II HLA antigens. In those two patients, the cPRA changed from 0% and 63% at baseline to 9% and 90% after vaccination, respectively. Both patients received the BNT162b2 mRNA-based vaccine. The earliest detected anti-HLA Abs was 18 days after the first dose. CONCLUSION: In rare cases, new anti-HLA antibodies were observed after COVID-19 vaccination, with potential implications for transplantation. The low incidence of this phenomenon is outweighed by the clinical benefits of vaccination.

Diagnostic outcomes of robotic-assisted bronchoscopy for pulmonary lesions in a real-world multicenter community setting.

BACKGROUND: Robot-assisted bronchoscopy (RAB) is among the newest bronchoscopic technologies, allowing improved visualization and access for small and hard-to-reach nodules. RAB studies have primarily been conducted at academic centers, limiting the generalizability of results to the broader real-world setting, while variability in diagnostic yield definitions has impaired the validity of cross-study comparisons. The objective of this study was to determine the diagnostic yield and sensitivity for malignancy of RAB in patients with pulmonary lesions in a community setting and explore the impact of different definitions on diagnostic yield estimates. METHODS: Data were collected retrospectively from medical records of patients ≥ 21 years who underwent bronchoscopy with the Monarch® Platform (Auris Health, Inc., Redwood City, CA) for biopsy of pulmonary lesions at three US community hospitals between January 2019 and March 2020. Diagnostic yield was calculated at the index RAB and using 12-month follow-up data. At index, all malignant and benign (specific and non-specific) diagnoses were considered diagnostic. After 12 months, benign non-specific cases were considered diagnostic only when follow-up data corroborated the benign result. An alternative definition at index classified benign non-specific results as non-diagnostic, while an alternative 12-month definition categorized index non-diagnostic cases as diagnostic if no malignancy was diagnosed during follow-up. RESULTS: The study included 264 patients. Median lesion size was 19.3 mm, 58.9% were peripherally located, and 30.1% had a bronchus sign. Samples were obtained via Monarch in 99.6% of patients. Pathology led to a malignant diagnosis in 115 patients (43.6%), a benign diagnosis in 110 (41.7%), and 39 (14.8%) non-diagnostic cases. Index diagnostic yield was 85.2% (95% CI: [80.9%, 89.5%]) and the 12-month diagnostic yield was 79.4% (95% CI: [74.4%, 84.3%]). Alternative definitions resulted in diagnostic yield estimates of 58.7% (95% CI: [52.8%, 64.7%]) at index and 89.0% (95% CI: [85.1%, 92.8%]) at 12 months. Sensitivity for malignancy was 79.3% (95% CI: [72.7%, 85.9%]) and cancer prevalence was 58.0% after 12 months. CONCLUSIONS: RAB demonstrated a high diagnostic yield in the largest study to date, despite representing a real-world community population with a relatively low prevalence of cancer. Alternative definitions had a considerable impact on diagnostic yield estimates.

Application of data mining to minimize fire-induced domino effect risks.

Data mining (DM) has been applied in many advanced science and technology fields, but it has still not been used for domino effect risk management to explore minimum risk scenarios. This work investigates the feasibility of DM in minimizing the risk of fire-induced domino effects in chemical processing facilities. Based on DM, an evidential failure mode and effects analysis (E-FMEA), which could bridge chemical facilities' operational reliability and domino effect risk, is combined with fault tree analysis (FTA) for the occurrence risk modeling of loss of containment (LOC) event of chemical facilities, which is often the triggering point of fire-induced domino effects. Industry specific data such as reliability data, inspection records, and maintenance records are of great value to model the potential occurrence criticality of LOC. The data are used to characterize the LOC risk priority number (RPN) of chemical facilities through FTA and E-FMEA, search and statistics rules are proposed to mine inspection records to assess LOC risk factors. According to the RPN scores of facilities, inherent safety strategies to minimize risk via inventory control are proposed, and their effectiveness is tested using a well-known probit model. In this way, the approach proposes a unit-specific evidence-based risk minimization strategy for fire-induced domino effects. A case study demonstrates the capability of DM in the risk minimization of fire-induced domino effects.

Textual data transformations using natural language processing for risk assessment.

Underlying information about failure, including observations made in free text, can be a good source for understanding, analyzing, and extracting meaningful information for determining causation. The unstructured nature of natural language expression demands advanced methodology to identify its underlying features. There is no available solution to utilize unstructured data for risk assessment purposes. Due to the scarcity of relevant data, textual data can be a vital learning source for developing a risk assessment methodology. This work addresses the knowledge gap in extracting relevant features from textual data to develop cause-effect scenarios with minimal manual interpretation. This study applies natural language processing and text-mining techniques to extract features from past accident reports. The extracted features are transformed into parametric form with the help of fuzzy set theory and utilized in Bayesian networks as prior probabilities for risk assessment. An application of the proposed methodology is shown in microbiologically influenced corrosion-related incident reports available from the Pipeline and Hazardous Material Safety Administration database. In addition, the trained named entity recognition (NER) model is verified on eight incidents, showing a promising preliminary result for identifying all relevant features from textual data and demonstrating the robustness and applicability of the NER method. The proposed methodology can be used in domain-specific risk assessment to analyze, predict, and prevent future mishaps, ameliorating overall process safety.

Evaluating Options and Ethics in Pediatric Dentistry due to Declining Access to Hospital Operating Rooms.

AbstractPediatric dentists rely on access to hospital operating rooms for safe, effective, and humane delivery of dental care. The children who benefit most from dental treatment in a hospital operating room are those who are very young, have dental anxieties or phobias, are precommunicative or noncommunicative, need extensive or invasive dental treatments, or have special healthcare needs. Diminishing access to hospital operating rooms for pediatric dental treatment has become an escalating problem in contemporary times. Financial barriers, hospital costs, reimbursement rates, health insurance policies and deductibles, out-of-network hospitals, socioeconomic factors, and the COVID-19 pandemic are prominent contributing factors. This problem in access to care has resulted in long waiting times for hospital operating rooms, deferral of medically necessary dental care, and pain and infection among this vulnerable patient population. Pediatric dentists have responded to the problem by utilizing alternative methods of care delivery, such as in-office deep sedation or in-office general anesthesia, and by implementing aggressive medical management of dental caries. However, the youngest of pediatric patients and children with special healthcare needs still remain at a disadvantage in receiving definitive dental treatment. This article aims to highlight the ethical challenges faced by pediatric dentists in contemporary practice in the face of limitations in hospital operating room access through four case scenarios.

Revealing analgesic and anxiolytic potentials of synthetic benzimidazole analogues: An in-vivo and in-silico study.

Certain drugs have potential to affect and alter individual's behavior. On the other hand, pain is a complex phenomenon with various treatment options; analgesic medicines are the primary source. Therefore, this study was based on examining some of the benzimidazole analogues for their analgesic as well as behavioral potential following Tail immersion test and Open field test respectively. In addition, molecular docking was performed to find the interaction of these compounds with the active site using AutoDock Vina which was further visualized through Discovery Studio Visualizer. It was seen that the cyano-methyl benzimidazole derivatives (CMB1-CMB3) showed relief in pain as compared to benzimidazole derivatives (BI1-BI3), CMB2 demonstrated highly potent analgesic effect. Likewise, all structures except BI1 displayed increase locomotion during open field test and can be offered as anxiolytic compounds. Almost all derivatives showed improve binding energies for the tested proteins where the high analgesic action of CMB2 might be correlated to its high binding affinity and interaction at µOR. It was also noticed that all structures except BI showed possible binding interaction with GABAA receptor and hence possessed anxiolytic like potential. Thus, this study offered benzimidazole analogues for further drug development of analgesic and anxiolytic like compounds.

Application and comparison of active and transfer learning approaches for modulation format classification in visible light communication systems.

Automatic modulation classification (AMC) is a crucial part of adaptive modulation schemes for visible light communication (VLC) systems. However, most of the deep learning (DL) based AMC methods for VLC systems require a large amount of labeled training data which is quite difficult to obtain in practical systems. In this work, we introduce active learning (AL) and transfer learning (TL) approaches for AMC in VLC systems and experimentally analyze their performances. Experimental results show that the proposed novel AlexNet-AL and AlexNet-TL methods can significantly improve the classification accuracy with small sizes of training data. To be specific, using 60 labeled samples, AlexNet-AL and AlexNet-TL increase the classification accuracy by 6.82% and 14.6% compared to the result without AL and TL, respectively. Moreover, the use of data augmentation (DA) operation along with our proposed methods helps achieve further better performances.

Impact of FLT3 internal tandem duplication and NPM1 mutations in acute myeloid leukemia treated with allogeneic hematopoietic cell transplantation.

BACKGROUND AIMS: The internal tandem duplication of FLT3 (FLT3ITD) and NPM1 mutations (NPM1mut) are well-established prognostic factors in cytogenetically intermediate-risk acute myeloid leukemia (AML) when treated with chemotherapy alone. However, their prognostic value in the setting of allogeneic hematopoietic cell transplantation (HCT) is controversial. METHODS: FLT3 and NPM1 mutational status was determined at diagnosis using single-gene polymerase chain reaction or next-generation sequencing in 247 adult patients with cytogenetically intermediate-risk AML who underwent myeloablative HCT. Multivariate Fine-Gray and Cox regression was used to analyze the cumulative incidence of relapse (CIR), relapse-free survival (RFS) and overall survival (OS). RESULTS: FLT3ITD and NPM1mut were present in 74 of 247 (30%) and 79 of 247 (32%) patients, respectively. There was no significant difference between patients without a FLT3ITD or NPM1mut (FLT3NONITD/NPM1WT) and patients with a FLT3ITD mutation alone (FLT3ITD/NPM1WT) with regard to CIR (P = 0.60), RFS (P = 0.91) or OS (P = 0.66). Similarly, there was no significant difference between FLT3NONITD/NPM1WT and FLT3NONITD/NPM1mut patients with regard to CIR (P = 0.70), RFS (P = 0.75) or OS (P = 0.95). The presence of a concurrent mutation in NPM1 did not appear to modify the impact of having a FLT3ITD mutation. CONCLUSIONS: In contrast to chemotherapy-only treatment, FLT3 and NPM1 mutational status does not appear to predict outcomes in patients with cytogenetically intermediate-risk AML following HCT. These results suggest that HCT may ameliorate the poor prognostic effect of FLT3ITD mutation and that HCT should be considered over chemotherapy-only treatment in FLT3ITD-mutated AML.

Incomplete chimerism following myeloablative and anti-thymocyte globulin-conditioned hematopoietic cell transplantation is a risk factor for relapse and chronic graft-versus-host disease.

BACKGROUND AIMS: The value of routine chimerism determination after myeloablative hematopoietic cell transplantation (HCT) is unclear, particularly in the setting of anti-thymocyte globulin (ATG)-based graft-versus-host disease (GVHD) prophylaxis. METHODS: Blood samples were collected at 3 months post-HCT from 558 patients who received myeloablative conditioning and ATG-based GVHD prophylaxis. Chimerism was assessed using multiplex polymerase chain reaction of short tandem repeats in sorted T cells (CD3+) and leukemia lineage cells (CD13+CD33+ for myeloid malignancies and CD19+ for B-lymphoid malignancies). ATG exposure was determined using a flow cytometry-based assay. The primary outcomes of interest were relapse and chronic GVHD (cGVHD). RESULTS: Incomplete (<95%) T-cell chimerism and leukemia lineage chimerism were present in 17% and 4% of patients, respectively. Patients with incomplete T-cell chimerism had a significantly greater incidence of relapse (36% versus 22%, subhazard ratio [SHR] = 2.03, P = 0.001) and lower incidence of cGVHD (8% versus 25%, SHR = 0.29, P < 0.001) compared with patients with complete chimerism. In multivariate modeling, patients with high post-transplant ATG area under the curve and any cytomegalovirus (CMV) serostatus other than donor/recipient seropositivity (non-D+R+) had an increased likelihood of incomplete T-cell chimerism. Patients with incomplete leukemia lineage chimerism had a significantly greater incidence of relapse (50% versus 23%, SHR = 2.70, P = 0.011) and, surprisingly, a greater incidence of cGVHD (45% versus 20%, SHR = 2.64, P = 0.003). CONCLUSIONS: High post-transplant ATG exposure and non-D+R+ CMV serostatus predispose patients to incomplete T-cell chimerism, which is associated with an increased risk of relapse. The increased risk of cGVHD with incomplete B-cell/myeloid chimerism is a novel finding that suggests an important role for recipient antigen-presenting cells in cGVHD pathogenesis.

Treatment and Outcome of Autoimmune Hepatitis (AIH): Audit of 28 UK centres.

BACKGROUND: With few data regarding treatment and outcome of patients with AIH outside of large centres we present such a study of patients with AIH in 28 UK hospitals of varying size and facilities. METHODS: Patients with AIH were identified in 14 University and 14 District General hospitals; incident cases during 2007-2015 and prevalent cases, presenting 2000-2015. Treatment and outcomes were analysed. RESULTS: In 1267 patients with AIH, followed-up for 3.8(0-15) years, 5- and 10-year death/transplant rates were 7.1+0.8% and 10.1+1.3% (all-cause) and 4.0+0.6% and 5.9+1% (liver-related) respectively. Baseline parameters independently associated with death/transplantation for all-causes were: older age, vascular/respiratory co-morbidity, cirrhosis, decompensation, platelet count, attending transplant centre and for liver-related: the last four of these and peak bilirubin All-cause and liver-related death/transplantation was independently associated with: non-treatment with corticosteroids, non-treatment with a steroid-sparing agent (SSA), non-treatment of asymptomatic or non-cirrhotic patients and initial dose of Prednisolone >35mg/0.5mg/kg/day (all-cause only), but not with type of steroid (Prednisolone versus Budesonide) or steroid duration beyond 12-months. Subsequent all-cause and liver-death/transplant rates showed independent associations with smaller percentage fall in serum ALT after 1 and 3-months, but not with failure to normalise levels over 12-months. CONCLUSIONS: We observed higher death/transplant rates in patients with AIH who were untreated with steroids (including asymptomatic or non-cirrhotic sub-groups), those receiving higher Prednisolone doses and those who did not receive an SSA. Similar death/transplant rates were seen in those receiving Prednisolone or Budesonide, those continuing steroids after 12-months and patients attaining normal ALT within 12-months versus not.

Assessment of donor cell engraftment after hematopoietic stem cell transplantation for sickle cell disease: A review of current and future methods.

Hematopoietic stem cell transplantation (HSCT) is the only established curative treatment for sickle cell disease (SCD), a debilitating red blood cell (RBC) disorder with significant prevalence worldwide. Accurate assessment of RBC engraftment following HSCT is essential to evaluate the status of the graft and can enable early intervention to treat or prevent graft rejection. Currently, chimerism measurement is performed on whole blood samples, which mainly reflect white blood cell (WBC) chimerism. This approach has limitations in assessing engraftment in patients with SCD because RBCs engraft non-linearly with WBCs. Direct measures of RBC chimerism exist but are not routinely used. In this review, we critically examine the current methodologies for assessing donor engraftment; highlight the limitations of these different methods, and present emerging and novel technologies with the potential to improve clinical monitoring of RBC engraftment post-HSCT for SCD. Promising alternative methodologies include RBC-specific flow cytometry, RBC-specific RNA analysis, and quantification of plasma cell-free DNA derived specifically from nucleated RBCs.

A Connectionist Model for Dynamic Economic Risk Analysis of Hydrocarbons Production Systems.

This study presents a connectionist model for dynamic economic risk evaluation of reservoir production systems. The proposed dynamic economic risk modeling strategy combines evidence-based outcomes from a Bayesian network (BN) model with the dynamic risks-based results produced from an adaptive loss function model for reservoir production losses/dynamic economic risks assessments. The methodology employs a multilayer-perceptron (MLP) model, a loss function model; it integrates an early warning index system (EWIS) of oilfield block with a BN model for process modeling. The model evaluates the evidence-based economic consequences of the production losses and analyzes the statistical disparities of production predictions using an EWIS-assisted BN model and the loss function model at the same time. The proposed methodology introduces an innovative approach that effectively minimizes the potential for dynamic economic risks. The model predicts real-time daily production/dynamic economic losses. The connectionist model yields an encouraging overall predictive performance with average errors of 1.954% and 1.957% for the two case studies: cases 1 and 2, respectively. The model can determine transitional/threshold production values for adequate reservoir management toward minimal losses. The results show minimum average daily dynamic economic losses of $267,463 and $146,770 for cases 1 and 2, respectively. It is a multipurpose tool that can be recommended for the field operators in petroleum reservoir production management related decision making.

Investigating the therapeutic options for diabetes-associated male infertility as illustrated in animal experimental models.

Diabetes is a rising global health concern and an increasingly common cause of male infertility. Although the definitive pathophysiological mechanisms underpinning the association between diabetes and infertility is unclear, there are several animal studies showing diabetes to be a detrimental factor on reproductive health through apoptosis, oxidative stress and impairment of steroidogenesis. Furthermore, as reflected in animal models, antidiabetic strategies and relevant treatments are beneficial in the management of infertile men with diabetes as the recovery of euglycemic status affects positively the spermatogenesis. However, the available data are still evolving and specific conclusion in human populations are not possible yet. In this review, we are discussing the current literature concerning the association of diabetes and male infertility, focusing on the therapeutic approach as illustrated in animals' models.

Accurate Step Count with Generalized and Personalized Deep Learning on Accelerometer Data.

Physical activity (PA) is globally recognized as a pillar of general health. Step count, as one measure of PA, is a well known predictor of long-term morbidity and mortality. Despite its popularity in consumer devices, a lack of methodological standards and clinical validation remains a major impediment to step count being accepted as a valid clinical endpoint. Previous works have mainly focused on device-specific step-count algorithms and often employ sensor modalities that may not be widely available. This may limit step-count suitability in clinical scenarios. In this paper, we trained neural network models on publicly available data and tested on an independent cohort using two approaches: generalization and personalization. Specifically, we trained neural networks on accelerometer signals from one device and either directly applied them or adapted them individually to accelerometer data obtained from a separate subject cohort wearing multiple distinct devices. The best models exhibited highly accurate step-count estimates for both the generalization (96-99%) and personalization (98-99%) approaches. The results demonstrate that it is possible to develop device-agnostic, accelerometer-only algorithms that provide highly accurate step counts, positioning step count as a reliable mobility endpoint and a strong candidate for clinical validation.

Metabolomics Study of Serum Samples of ß-YAC Transgenic Mice Treated with Tenofovir Disoproxil Fumarate.

ß-thalassemia is one of the most common monogenic disorders and a life-threatening health issue in children. A cost-effective and safe therapeutic approach to treat this disease is to reactivate the γ-globin gene for fetal hemoglobin (HbF) production that has been silenced during infancy. Hydroxyurea (HU) is the only FDA approved HbF inducer. However, its cytotoxicity and inability to respond significantly in all patients pose a need for an HbF inducer with better efficacy. The study describes the serum metabolic alteration in ß-YAC transgenic mice treated with Tenofovir disoproxil fumarate (TDF) (n = 5), a newly identified HbF inducer, and compared to the mice groups treated with HU (n = 5) and untreated control (n = 5) using gas chromatography-mass spectrometry. Various univariate and multivariate statistical analyses were performed to identify discriminant metabolites that altered the biological pathways encompassing galactose metabolism, lactose degradation, and inositol. Furthermore, the decreased concentrations of L-fucose and geraniol in TDF-treated mice help in recovering towards normal, decreasing oxidative stress even much better than the HU-treated mice. The proposed study suggested that TDF can reduce the deficiency of blood required for ß-thalassemia and can be used for the preclinical study at phase I/II for fetal hemoglobin production.