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Abstract We experimentally investigated the self-pulsing (SP) oscillations induced by the thermo-optic, free carrier, and Kerr nonlinear effects in integrated active silicon microring resonators. We demonstrate high frequency self-pulsing oscillations (up to 30 MHz) by applying a few millivolts of reverse bias voltage to the PIN junction of the active cavity. We illustrate that the shape of those oscillations (i.e., frequency and duty cycle) can be controlled by adjusting the CW input power and applying a reverse bias voltage to the PIN junction for carrier removal. This controlling is important for synchronizing the cavity which is crucial for neural network applications. Furthermore, we utilize a mathematical model for visualizing the stability regions by numerically studying coupled mode theory in silicon microcavity under different conditions.

Abstract Hope is a vital coping mechanism, enabling individuals to effectively confront life’s challenges. This study proposes a technique employing Natural Language Processing (NLP) tools like Linguistic Inquiry and Word Count (LIWC), NRC-emotion-lexicon, and vaderSentiment to analyze social media posts, extracting psycholinguistic, emotional, and sentimental features from a hope speech dataset. The findings of this study reveal distinct cognitive, emotional, and communicative characteristics and psycholinguistic dimensions, emotions, and sentiments associated with different types of hope shared in social media. Furthermore, the study investigates the potential of leveraging this data to classify different types of hope using machine learning algorithms. Notably, models such as LightGBM and CatBoost demonstrate impressive performance, surpassing traditional methods and competing effectively with deep learning techniques. We employed hyperparameter tuning to optimize the models’ parameters and compared their performance using both default and tuned settings. The results highlight the enhanced efficiency achieved through hyperparameter tuning for these models.

Abstract Corneal ulcers, whether melting or indolent, are common in humans and companion animals. Treatment involves local administration of antibiotic eye drops and corneal healing drugs. Compared to traditional treatments for ulcerative keratitis, herbal medicines offer unique advantages, such as potent anti-inflammatory effects and inhibition of proinflammatory cytokines. Curcumin, extracted from the Curcuma Longa plant, possesses extensive pharmacological properties, such as anti-inflammatory, anti-cancer, and antioxidant properties, and is used in various medicines. In this study, we developed a novel ophthalmic drop hydrogel using a formulation of Curcumin NPs encapsulated with β-cyclodextrin and hyaluronic acid, to accelerate corneal healing and improve the quality of healed structures. The formation of Curcumin NPs into Hyaluronic acid-based hydrogels was characterized by zeta, FTIR, and scanning electron microscope (SEM) analyses. A total of 25 healthy male New Zealand Albino rabbits were experimentally induced with ulcerative keratitis and treated individually with topical medication. Rabbits were divided into five groups. Fluorescein dye staining, corneal clarity score, Schirmer tear test, proinflammatory cytokine measurement, and pathologic factors assessments were used to evaluate the optimised Curcumin NPs with β-cyclodextrin in Hyaluronic acid hydrogel. Our results demonstrated that the optimized Curcumin NPs with β-cyclodextrin in hyaluronic acid hydrogel significantly reduced the frequency of medication administration compared to conventional therapies, enhancing the quality of healed structures and effectively treating ulcerative keratitis. All findings in this study provide new insight into designing and fabricating novel ophthalmic medicine for ulcerative keratitis for topical usage.

Abstract Concrete, as a cornerstone of modern construction, heavily relies on the quality of its constituent materials, particularly aggregates. Among the critical factors contributing to high-quality concrete are proper gradation, absence of clay particles, and angular shape of aggregates. Adhering to these standards typically results in concrete with superior strength. However, aggregates sourced from riverbeds often possess a natural gradation, contain clay particles, and have rounded shapes. This study delves into a comparative analysis of aggregates sourced from two widely utilized riverbed regions, namely Hari-River and Kamar-Kalaq, situated within Herat province, Afghanistan. Given that over 90% of concrete in Herat province is sourced from these two riverbeds, the findings of this study carry immense significance. The research meticulously examines key parameters, including clay content, gradation, aggregate shape, and compressive strength, to determine the optimal choice for concrete production. Methodologically, samples were acquired following ASTM standards, and rigorous testing procedures were conducted, encompassing clay particle analysis, sieve analysis, and strength testing. The results reveal significant disparities between the two regions, with Hari-River demonstrating superior characteristics across various metrics. Particularly noteworthy is Hari-River’s lower clay content of 2.7% compared to Kamar-Kalaq’s 3.7%. The gradation of Hari-River for both coarse and fine aggregates is superior to that of Kamar-Kalaq when compared to size 67 aggregate range. Additionally, the average 28 days concrete compressive strength of Hari-River aggregates is 27.8 MPa, while that of Kamar-Kalaq is 23.4 MPa.

Abstract Wellbore stability analysis is a critical component of petroleum engineering, evaluating the risks of sanding, reservoir compaction, and casing failures. Laboratory rock mechanical measurements must be scaled up to reservoir scales to achieve accurate results. One challenge lies in upscaling dynamic measurements from petrophysical logs to pseudo-static elastic properties, which has significant implications for oil and gas operations. We present a novel approach that combines laboratory rock mechanical measurements with well-log data to develop a mechanical earth model (MEM) for an Iranian oilfield with over 350 wells. We conducted static elastic property measurements on 40 core samples from various layers and depths of carbonate and sandstone rocks, demonstrating the practical application of our approach. By integrating these measurements with dynamic log data and static-dynamic correlations, we established a framework for evaluating the mechanical properties of different layers. Our findings indicate that the safe mud weight window ranges from 41.5 to 118.59 pcf, while the stable mud weight window ranges from 41.5 to 156 pcf. We demonstrate the importance of conducting parallel rock mechanical studies on cores and logs to reduce uncertainties, costs, and risks during oil and gas operations. We also propose a novel methodology combining lithological characteristics, abnormally high pressure, and borehole instability mechanisms to evaluate the stability of borehole walls. This framework provides a fresh perspective on wellbore stability analysis and offers practical solutions for the industry. Essential novel techniques include developing a geomechanical model that integrates laboratory rock mechanical measurements with well-log data to evaluate mechanical properties and calculate safe and stable mud-weight windows. Our study advances wellbore stability analysis by providing a new method for addressing this long-standing challenge. It offers valuable insights for petroleum engineers working in the oil and gas industry.

Abstract Cardiometabolic risk factors increase the chance of developing cardiovascular disease (CVD) and type 2 diabetes. Most CVD risk factors are influenced by total and regional obesity. A higher risk of developing CVD may be linked to vitamin D deficiency, which is more prevalent in the older population. With the goal of evaluating the association between vitamin D and cardiometabolic risk factors and total and regional obesity in older adults, this research included 25 (OH) vitamin D3 concentrations and biochemical markers associated with cardiometabolic diseases, as well as total and regional adiposity, which was measured by DXA. A total of 1991 older participants in the PoCOsteo study were included. Overall, 38.5% of participants had vitamin D deficiency. After adjusting for confounders, the results of multiple linear and logistic regression suggested an inverse association between vitamin D and body mass index (P = 0.04), waist circumference (P = 0.001), total fat (P = 0.02), android fat (P = 0.001), visceral fat (P

Abstract Alcohol production and consumption have been prohibited in Iran for over four decades, leading to a typical underestimation of its consumption. This study aimed to assess the prevalence of alcohol consumption, its associated factors, and estimate per capita alcohol consumption among Iran’s adult population. In this population-based survey, 27,874 adults from across Iran were selected using systematic proportional-to-size cluster sampling. Alcohol consumption was evaluated through a modified Persian version of the STEPS questionnaires from previous studies, applied over different timespans. Per capita consumption was calculated using the quantity-frequency method, expressed in liters of pure alcohol. Adjusted odds ratios were reported for associates of alcohol consumption concerning metabolic risk factors, sociodemographic elements, and lifestyle variables. The prevalence of lifetime alcohol consumption was 6.9% (95% CI 6.5–7.2) in the adult population, with a notable sex difference (males: 13.7% [95% CI 13–14.4]; females: 1.4% [95% CI 1.1–1.6]). The 12 month prevalence was 3.8% (95% CI 3.6–4.1). For individuals aged 18 and older, the per capita alcohol consumption in Iran was 0.12 L. Factors such as being a lifetime smoker, younger, wealthier, and having 7–12 years of education were significantly linked to higher alcohol consumption. Significant associations were also observed between alcohol consumption and having a history of heart attacks (OR = 2.04, 95% CI 1.44–2.89), and physical injuries (OR = 1.88, 95% CI 1.34–2.64). The estimated lifetime and 12-month prevalence of alcohol use in our study were higher among some of the subpopulations. The findings also revealed a complex relationship between alcohol consumption, behavioral risk factors, and metabolic profiles. Consequently, immediate preventive measures tailored to each factor’s association with alcohol use are recommended.

Abstract Postoperative intra-abdominal adhesions represent a significant post-surgical problem. Its complications can cause a considerable clinical and cost burden. Herein, our study aimed to investigate the effect of Everolimus on peritoneal adhesion formation after inducing adhesions in rats. In this experimental study, adhesion bands were induced by intraperitoneal injection of 3 ml of 10% sterile talc solution in 64 male albino rats. The first group served as the control group. The second one received oral Prednisolone (1 mg/kg/day), the third received Everolimus (0.1 mg/kg/day), and group four received both drugs with similar dosages for four consecutive weeks. The formation of adhesion bands was qualitatively graded according to the Nair classification. The rats in the control group had extensive adhesions between the abdominal wall and the organs. Regarding substantial adhesion formation, 50% (8/16) of animals in the control group had substantial adhesions, while this rate in the groups receiving Prednisolone, Everolimus, and combination treatment was 31%, 31%, and 31%, respectively. Also, 68.75% (5/11) of the Prednisolone recipients had insubstantial adhesions, the same as Everolimus recipients, while in the combination group, 66.66% (10/15) rats had insubstantial adhesions. Everolimus demonstrated satisfactory results in reducing the rates of induced peritoneal adhesion in an experimental model, similar to Prednisolone and superior to a combination regime.

Decreased libido and anorgasmia are common problems for women after menopause that reduce the quality of life of couples. This study examined the effect of topical Qust oil on sexual desire disorder in postmenopausal women. In this double-blind, randomized, clinical trial, 110 postmenopausal women with decreased sexual desire visiting a Traditional Medicine Center and Hazrat Rasool Akram Hospital (affiliated to Iran University of Medical Sciences) were selected by convenience consecutive sampling and randomly assigned to experimental and control groups. The experimental group received qust oil, while the control group was given a placebo (liquid paraffin); they were instructed to massage the product topically on their pubic area and perineum daily. The sexual function of both groups was assessed and compared before the intervention and four weeks after the intervention using the Female Sexual Function Index. The mean and standard deviation of the improvement of sexual function post-intervention were 37.66±32.52% and 11.96±11.18% in the experimental and control groups, respectively (p

Mass cytometry is a cutting-edge high-dimensional technology for profiling marker expression at the single-cell level, advancing clinical research in immune monitoring. Nevertheless, the vast data generated by cytometry by time-of-flight (CyTOF) poses a significant analytical challenge. To address this, we describe ImmCellTyper (https://github.com/JingAnyaSun/ImmCellTyper), a novel toolkit for CyTOF data analysis. This framework incorporates BinaryClust, an in-house developed semi-supervised clustering tool that automatically identifies main cell types. BinaryClust outperforms existing clustering tools in accuracy and speed, as shown in benchmarks with two datasets of approximately 4 million cells, matching the precision of manual gating by human experts. Furthermore, ImmCellTyper offers various visualisation and analytical tools, spanning from quality control to differential analysis, tailored to users’ specific needs for a comprehensive CyTOF data analysis solution. The workflow includes five key steps: (1) batch effect evaluation and correction, (2) data quality control and pre-processing, (3) main cell lineage characterisation and quantification, (4) in-depth investigation of specific cell types; and (5) differential analysis of cell abundance and functional marker expression across study groups. Overall, ImmCellTyper combines expert biological knowledge in a semi-supervised approach to accurately deconvolute well-defined main cell lineages, while maintaining the potential of unsupervised methods to discover novel cell subsets, thus facilitating high-dimensional immune profiling.

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and memory loss. Imipramine, a tricyclic antidepressant, has potent anti-inflammatory and antioxidant properties in the central nervous system. The aim of this study was to investigate the neuroprotective effects of imipramine on streptozotocin (STZ)-induced memory impairment. Male Wistar rats received an intracerebroventricular injection of STZ (3 mg/kg, 3 μl/ventricle) using the stereotaxic apparatus. The Morris water maze and passive avoidance tests were used to evaluate cognitive functions. 24 h after the STZ injection, imipramine was administered intraperitoneally at doses of 10 or 20 mg/kg for 14 consecutive days. The mRNA and protein levels of neurotrophic factors (BDNF and GDNF) and pro-inflammatory cytokines (IL-6, IL-1β, and TNF-α) were measured in the hippocampus using real-time PCR and ELISA techniques, respectively. In addition, real-time PCR was used to evaluate the mRNA levels of markers associated with neurogenesis (Nestin, DCX, and Ki67) and mitochondrial biogenesis (PGC-1α, NRF-1, and TFAM). The results showed that imipramine, especially at a dose of 20 mg/kg, effectively improved STZ-induced memory impairment. This improvement was associated with an increase in neurogenesis and neurotrophic factors and a decrease in neuroinflammation and mitochondrial biogenesis dysfunction. Based on these results, imipramine appears to be a promising therapeutic option for improving cognitive functions in neurodegenerative diseases such as AD.

Abstract Background High prostate eicosapentaenoic fatty acid (EPA) levels were associated with a significant reduction of upgrading to grade group (GG) ≥ 2 prostate cancer in men under active surveillance. We aimed to evaluate the effect of MAG-EPA long-chain omega-3 fatty acid dietary supplement on prostate cancer proliferation. Methods A phase II double-blind randomized placebo-controlled trial was conducted in 130 men diagnosed with GG ≥ 2 prostate cancer and undergoing radical prostatectomy between 2015–2017 (Clinicaltrials.gov: NCT02333435). Participants were randomized to receive 3 g daily of either MAG-EPA (n = 65) or placebo (n = 65) for 7 weeks (range 4–10) prior to radical prostatectomy. The primary outcome was the cancer proliferation index quantified by automated image analysis of tumor nuclear Ki-67 expression using standardized prostatectomy tissue microarrays. Additional planned outcomes at surgery are reported including plasma levels of 27 inflammatory cytokines and fatty acid profiles in circulating red blood cells membranes and prostate tissue. Results Cancer proliferation index measured by Ki-67 expression was not statistically different between the intervention (3.10%) and placebo (2.85%) groups (p = 0.64). In the per protocol analyses, the adjusted estimated effect of MAG-EPA was greater but remained non-significant. Secondary outcome was the changes in plasma levels of 27 cytokines, of which only IL-7 was higher in MAG-EPA group compared to placebo (p = 0.026). Men randomized to MAG-EPA prior to surgery had four-fold higher EPA levels in prostate tissue compared to those on placebo. Conclusions This MAG-EPA intervention did not affect the primary outcome of prostate cancer proliferation according to nuclear Ki-67 expression. More studies are needed to decipher the effects of long-chain omega-3 fatty acid dietary supplementation in men with prostate cancer.

Abstract This study aimed to investigate the biological activities of Lactobacillus gasseri SM 05 (L. gasseri) and Lacticaseibacillus casei subsp. casei PTCC 1608 (L. casei) in the black raspberry (Rubus dolichocarpus) juice (BRJ) environment, and also the anti-adhesion activity against Salmonella typhimurium (S. typhimurium) in fermented black raspberry juice (FBRJ). Results showed significant anti-adhesion activity in Caco-2 epithelial cells. In the anti-adhesion process, lactic acid bacteria (LAB) improve intestinal health by preventing the adhesion of pathogens. Adding LAB to BRJ produces metabolites with bacteriocin properties. Major findings of this research include improved intestinal health, improved antidiabetic properties, inhibition of degradation of amino acids, and increase in the nutritional value of foods that have been subjected to heat processing by preventing Maillard inhibition, and inhibition of oxidation of foodstuff by increased antioxidant activity of BRJ. Both species of Lactobacillus effectively controlled the growth of S. typhimurium during BRJ fermentation. Moreover, in all tests, as well as Maillard's and α-amylase inhibition, L. gasseri was more effective than L. casei. The phenolic and flavonoid compounds increased significantly after fermentation by both LAB (p

Abstract Spiking networks, as the third generation of neural networks, are of great interest today due to their low power consumption in cognitive processes. This important characteristic has caused the hardware implementation techniques of spiking networks in the form of neuromorphic systems attract a lot of attention. For the first time, the focus is on the digital implementation based on CORDIC approximation of the Hindmarsh–Rose (HR) neuron so that the hardware implementation cost is lower than previous studies. If the digital design of a neuron is done efficient, the possibility of implementing a population of neurons is provided for the feasibility of low-consumption implementation of high-level cognitive processes in hardware, which is considered in this paper through edge detector, noise removal and image magnification spiking networks based on the proposed CORDIC_HR model. While using less hardware resources, the proposed HR neuron model follows the behavior of the original neuron model in the time domain with much less error than previous study. Also, the complex nonlinear behavior of the original and the proposed model of HR neuron through the bifurcation diagram, phase space and nullcline space analysis under different system parameters was investigated and the good follow-up of the proposed model was confirmed from the original model. In addition to the fact that the individual behavior of the original and the proposed neurons is the same, the functional and behavioral performance of the randomly connected neuronal population of original and proposed neuron model is equal. In general, the main contribution of the paper is in presenting an efficient hardware model, which consumes less hardware resources, follows the behavior of the original model with high accuracy, and has an acceptable performance in image processing applications such as noise removal and edge detection.

The current study aims to evaluate the potential role of extracorporeal shock wave therapy (ESWT) on functional status, physical disability, and pain in patients suffering from low back pain (LBP). ESWT may provide superior therapeutic outcomes in reducing pain and ameliorating the patient's functional status regarding LBP.

We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.

Abstract Omicron variant (B.1.1.529) is able to escape from naturally acquired and vaccine-induced immunity, which mandates updating the current COVID-19 vaccines. Here, we investigated and compared the neutralising antibody induction of the ancestral variant-based BIV1-CovIran vaccine, the Omicron variant-based BIV1-CovIran Plus vaccine, and the novel bivalent vaccine candidate, BBIV1-CovIran, against the Omicron and ancestral Wuhan variants on the rat model. After inactivating the viral particles, the viruses were purified and formulated. Bivalent vaccines were a composition of 2.5 µg (5 µg total) or 5 µg (10 µg total) doses of each ansectral-based and Omicron-based monovalent vaccine. Subsequently, the potency of the monovalent and bivalent vaccines was investigated using the virus neutralisation test (VNT). The group that received three doses of the Omicron-specific vaccine demonstrated neutralisation activity against the Omicron variant with a geometric mean titer of 337.8. However, three doses of the Wuhan variant-specific vaccine could neutralise the Omicron variant at a maximum of 1/32 serum dilution. The neutralisation activity of the Omicron-specific vaccine, when administered as the booster dose after two doses of the Wuhan variant-specific vaccine, was 100% against the Omicron variant and the Wuhan variant at 1/64 and 1/128 serum dilution, respectively. Three doses of 5 µg bivalent vaccine could effectively neutralise both variants at the minimum of 1/128 serum dilution. The 10 µg bivalent vaccine at three doses showed even higher neutralisation titers: the geometric mean of 388 (95% CI 242.2–621.7) against Omicron and 445.7 (95% CI 303.3–655.0) against Wuhan. It is shown that the candidate bivalent and Omicron-specific vaccines could elicit a potent immune response against both Wuhan-Hu-1 and Omicron BA.1 variants.

Abstract The unique potential of fullerene C60 for various biological applications has ignited significant interest. However, its inherent non-polarity poses a critical challenge for its effective integration within biological systems. This study delves into the intricate physicochemical characteristics of the innovative [C60 + NO] complex using density functional theory and time-dependent density functional theory. The computational analyses encompass molecular charge, surface electrostatic potential, and dipole moment evaluations. Impressively, the dipole moment of the [C60 + NO] complex significantly increases to 12.92 D. Meticulous surface analysis reveals a subtle interplay between molecular structures, indicating weak interactions. The analysis of the absorption spectrum unveils a noteworthy red-shift of 200 nm subsequent to complex formation. To elucidate the electron transfer mechanisms, we explore photo-induced electron transfer through CAM-B3LYP. This exploration elucidates intricate pathways governing electron transfer, with complementary insights gleaned from Marcus theory's outputs, especially the Gibbs free energy of electron transfer. Changes in the physicochemical properties of approaching C60 and NO molecules reveal interesting results compared to separate molecules. These findings resonate profoundly in the context of potential biological and pharmaceutical utilization. With implications for the biomedical area, the outcomes linked to the [C60 + NO] complex kindle optimism for pioneering biomedical applications.

Background and Aim: Radiotherapy is a major modality for cancer treatment. One of the most important complications of breast radiotherapy is esophagitis. Esophagitis can reduce patient’s quality of life. Because of increasing role of radiotherapy in cancer treatment and importance of attention to quality of life in cancer patients, evaluation of radiotherapy complications is essential. Materials and Methods: This was a descriptive analytic study and included 34 breast cancer patients who had referred to the radiotherapy center of Tohid Hospital in Sanandaj and received radiation therapy. Based on a standard questionnaire of esophagus complications, patients were assessed for the symptoms of esophagitis for six consecutive weeks. In addition to demographic characteristics and assigning a code to every patient and weekly follow ups for searching esophagitis symptoms, amount of received radiation dose and other dosimetric indecies were calculated and the results were analyzed. Results: According to the results, mean prescription dose was 50 Gy and the average of mean dose and maximum dose received by esophagus were 2.60 and 29.78 Gy respectively. The most and least rates of complications were found in the third and first weeks respectively. Conclusion: In the patients who had received breast radiation therapy in the radiotherapy center of Tohid Hospital in Sanandaj in 2020, incidence of esophagitis was significantly correlated with the mean dose of radiation.

Abstract The resident human skin microbiome is responsible for the production of most of the human scents that are attractive to mosquitoes. Hence, engineering the human skin microbiome to synthesize less of mosquito attractants or produce repellents could potentially reduce bites and prevent the transmission of deadly mosquito-borne pathogens. In order to further characterize the human skin volatilome, we quantified the major volatiles of 39 strains of skin commensals (Staphylococci and Corynebacterium). Importantly, to validate the behavioral activity of these volatiles, we first assessed landing behavior triggered by human skin volatiles. We demonstrated that landing behavior is gated by the presence of carbon dioxide and L-(+)-lactic acid. This is similar to the combinatorial coding triggering mosquito short range attraction. Repellency behavior to selected skin volatiles and terpenes was tested in the presence of carbon dioxide and L-(+)-lactic acid. In a 2-choice landing behavior context, the skin volatiles 2- and 3-methyl butyric acids reduced mosquito landing by 62.0–81.6% and 87.1–99.6%, respectively. Similarly, the terpene geraniol was capable of reducing mosquito landing behavior by 74.9%. We also tested the potential repellency effects of terpenes in mosquitoes at short-range using a 4-port olfactometer. In these assays, geraniol reduced mosquito attraction (69–78%) to a mixture of key human kairomones carbon dioxide, L-(+)-lactic acid, and ammonia. These findings demonstrate that carbon dioxide and L-(+)-lactic acid change the valence of other skin volatiles towards mosquito landing behavior. Moreover, this study offers candidate odorants to be targeted in a novel strategy to reduce attractants or produce repellents by the human skin microbiota that may curtail mosquito bites, and subsequent mosquito-borne disease.

Abstract The lithology log, an integral component of the master log, graphically portrays the encountered lithological sequence during drilling operations. In addition to offering real-time cross-sectional insights, lithology logs greatly aid in correlating and evaluating multiple sections efficiently. This paper introduces a novel workflow reliant on an enhanced weighted average ensemble approach for producing high-resolution lithology logs. The research contends with a challenging multiclass imbalanced lithofacies distribution emerging from substantial heterogeneities within subsurface geological structures. Typically, methods to handle imbalanced data, e.g., cost-sensitive learning (CSL), are tailored for issues encountered in binary classification. Error correcting output code (ECOC) originates from decomposition strategies, effectively breaking down multiclass problems into numerous binary subproblems. The database comprises conventional well logs and lithology logs obtained from five proximate wells within a Middle Eastern oilfield. Utilizing well-known machine learning (ML) algorithms, such as support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and extreme gradient boosting (XGBoost), as baseline classifiers, this study aims to enhance the accurate prediction of underground lithofacies. Upon recognizing a blind well, the data from the remaining four wells are utilized to train the ML algorithms. After integrating ECOC and CSL techniques with the baseline classifiers, they undergo evaluation. In the initial assessment, both RF and SVM demonstrated superior performance, prompting the development of an enhanced weighted average ensemble based on them. The comprehensive numerical and visual analysis corroborates the outstanding performance of the developed ensemble. The average Kappa statistic of 84.50%, signifying almost-perfect agreement, and mean F-measures of 91.04% emphasize the robustness of the designed ensemble-based workflow during the evaluation of blind well data.

Introduction. Takotsubo cardiomyopathy (TCM) features transient left ventricular apical dysfunction or ballooning. The underlying mechanism remains elusive; however, evidence suggests the role of different physical and psychological stressors. We systematically reviewed patients presenting with TCM and autoimmunity to explore the link between the two conditions. Methods. We applied the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) to report this review. Using keywords related to autoimmune/immune-mediated diseases and TCM, we searched PubMed, Scopus, and WOS in March 2022. The final results were added to a data extraction sheet. Data were analyzed by SPSS version 26.0. Results. Our search yielded 121 studies, including 155 patients. Females were considerably predominant. Most patients had a history of autoimmune disease, and almost a third had a history of cardiovascular disease. Dyspnea and chest pain were the most common chief complaints. More than 70% of patients had experienced physical stress. Myasthenia gravis, systemic lupus erythematosus, and multiple sclerosis were the most frequently reported autoimmune diseases. Conclusion. There were similarities in age and sex compared to classic TCM. TCM should be considered as a differential diagnosis for ACS, especially in patients with a positive background of autoimmunity. A precise reporting system is required for further studies.

not needed for editorial

Kienbock disease (KD) was described by Robert Kienbock in 1910 as osteomalacia of the lunate. A century has passed since the first description of KD, and numerous theories, classifications, and treatments have been published for achieving the best outcomes for this disease, but the treatments remain controversial among surgeons. Various classifications have been proposed for KD based on radiography, MRI, arthroscopy, and morphology from 1947 to 2017. Recently, the pioneers of KD (Lichtman and Bain) proposed a new classification based on all the previous classification (radiographic, MRI, and Arthroscopy). This classification seems to be the best evaluation method and treatment for KD. We recommend using this new classification for assessment of Kienbock disease.

Seyed ahmad Sajjadi1 , Zahra Moosavi2 , Farhad Niknejad3 , Abdollah Jamshidi 4 Background: Aflatoxin B1 (AFB1) is one of the most important mycotoxins that contaminate food worldwide. Long-term consumption of foods contaminated with AFB1 endangers human health. Detoxification of AFB1 from food improves community health. A Specific approach to aflatoxin reduction is the use of probiotics. Kefir drink is a strong probiotic. The purpose of this study was to investigate the protective effect of kefir drink on AFB1-induced hepatic injury in adult male rats Methods: In this experimental study, 24 adult rats weighing between 150 and 200 g were used. The rats were randomly divided into 4 groups: 1) control, 2) AFB1 (50 μg/kg body weight), 3) kefir drink (10 mL/kg body weight), and 4) AFB1 + kefir drink. Aflatoxin and kefir drink received through oral gavage. At the end of the experiment (8 weeks), blood and liver samples were collected for different assays. Liver function tests and histopathological examinations were performed. Data were analyzed using 1-way analysis of variance (ANOVA) and at a significance level of

Background: After unprecedented demand for ethanol in the pandemic, profiteers used methanol for making illegal alcoholic drink. Methods: This cross-sectional descriptive-analytical study investigated the effects of methanol poisoning on 400 patients who referred to hospitals affiliated with Ahvaz Jundishapur University of Medical Sciences from March 20 to September 20, 2020. Results: Ninety-eight-point five percent of patients had consumed alcohol for social reasons and only 0.3% had used it as a measure to avoid COVID-19. Eighty-seven percent of the patients used homemade alcohol bought from peddlers. The most common clinical symptom was gastrointestinal disorders (64.8%) and the most common complications were vision problems (12.3%). Ten-point-six percent (42 patients) of the patients died. The most important factors affecting mortality risk were dyspnea, neuropathic problems, abnormal radiological findings, dialysis, abnormal blood pressure, vision problems and dizziness. Conclusions: Informing the public about the risks of using homemade alcoholic beverages is essential if the associated disability and mortality has to be avoided.

In the era of digitalization and artificial intelligence (AI), the utilization of Metaverse technology has become increasingly crucial. As the world becomes more digitized, there is a pressing need to effectively transfer real-world assets into the digital realm and establish meaningful relationships between them. However, existing approaches have shown significant limitations in achieving this goal comprehensively. To address this, this research introduces an innovative methodology called the Meta-Metaverse, which aims to enhance the immersive experience and create realistic digital twins across various domains such as biology, genetics, economy, medicine, environment, gaming, digital twins, Internet of Things, artificial intelligence, machine learning, psychology, supply chain, social networking, smart manufacturing, and politics. The multi-layered structure of Metaverse platforms and digital twins allows for greater flexibility and scalability, offering valuable insights into the potential impact of advancing science, technology, and the internet. This article presents a detailed description of the proposed methodology and its applications, highlighting its potential to transform scientific research and inspire groundbreaking ideas in science, medicine, and technology. [ABSTRACT FROM AUTHOR]