Antiseizure Activity of Mitragyna inermis in the Pentylenetetrazol-Induced Seizure Model in Mice: Involvement of Flavonoids and Alkaloids.

Objective: This study aimed to investigate whether Mitragyna inermis (Willd.) Otto Kuntze organic and aqueous extracts are able to control seizures induced by pentylenetetrazol (PTZ) in mice based on flavonoid fingerprints and alkaloidal contents. Materials and Methods: Ethanolic extract and decoction-derived fractions from roots, leaves, and stems were subjected to chromatographic fingerprinting using AlCl3 and screening for their antiseizure effects using PTZ-induced acute seizure model. From the fractions that showed potent bioactivities, plausible antiseizure alkaloids were isolated using thin layer chromatography, and their structures were elucidated using 1H NMR, 2D NMR, 13C NMR, and FAB-HR (+ve or -ve). Results: All fractions, with the exception of the dichloromethane and hexane fractions, revealed remarkable flavonoid fingerprints. An acute PTZ-induced seizure test revealed that ethanolic extract of stem bark [500 mg/kg body weight (bw)], ethyl acetate extract of stem bark (500 mg/kg bw), and aqueous extract of leaves (300 mg/kg bw) significantly delayed the occurrence of hind limb tonic extension (HLTE); however, a non-significant delay was observed in the onset of first myoclonic jerk compared with control animals. Isolation yielded four main alkaloids: that are, pteropodine (1), isopteropodine (2), mitraphylline (3) and corynoxeine (4). Corynoxeine is a new compound derived from M. inermis. Conclusion: This study suggests that flavonoid fingerprints are tracers of M. inermis anticonvulsant ingredients. The stem bark ethanolic and ethyl acetate extracts and leaf aqueous extracts contain anticonvulsant bioactive principles that delay notifying the HLTE occurring in male naval medical research institute mice. Furthermore, alkaloidal contents also remain plausible bioactive anticonvulsant principles. All observations support the traditional use of M. inermis to manage epilepsy. However, further studies are needed to understand the effects of alkaloid fractions, flavonoids, and the isolated compounds as promising antiseizure agents derived from M. inermis in experimental animals.

Electron plasma diagnostics in ELTRAP by electron cyclotron resonance heating method.

Electron cyclotron resonance heating method of Particle-in-Cell code was used to analyze heating phenomena, axial kinetic energy, and self-consistent electric field of confined electron plasma in ELTRAP device by hydrogen and helium background gases. The electromagnetic simulations were performed at a constant power of 3.8 V for different RF drives (0.5 GHz- 8 GHz), as well as for 1 GHz constant frequency at these varying amplitudes (1 V-3.8 V). The impacts of axial and radial temperatures were found maximum at 1.8 V and 5 GHz as compared to other amplitudes and frequencies for both background gases. These effects are higher at varying radio frequencies due to more ionization and secondary electrons production and maximum recorded radial temperature for hydrogen background gas was 170.41 eV. The axial kinetic energy impacts were found more effective in the outer radial part (between 0.03 and 0.04 meters) of the ELTRAP device due to applied VRF through C8 electrode. The self-consistent electric field was found higher for helium background gas at 5 GHz RF than other amplitudes and radio frequencies. The excitation and ionization rates were found to be higher along the radial direction (r-axis) than the axial direction (z-axis) in helium background gas as compared to hydrogen background gas. The current studies are advantageous for nuclear physics applications, beam physics, microelectronics, coherent radiation devices and also in magnetrons.

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.

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.

Impacts of crude oil market on global economy: Evidence from the Ukraine-Russia conflict via fuzzy models.

The increasing Russia-Ukraine crisis is without a doubt Europe's most prominent conflict since World War II, changing the dynamics of the oil and other key markets. Because the oil market has traditionally interacted with other financial and commodity markets, it will be intriguing to examine how it interacts with substantial financial assets amid market volatility induced by a conflict. The goal of this study is to propose a fuzzy time series (FTS) model and to compare its competitiveness to existing fuzzy time series (FTS) models, Autoregressive Integrated Moving Average (ARIMA) model and some machine learning methods i.e. Artificial Neural Networks (ANN), Support Vector Machine (SVM) and XGBoost models. We considered changes in the partitioning universe of discourse, optimization of parameters method(s), and interval estimation to make the forecast accuracy more precise forecasting than traditional methods via MAPE. The event-based data results show the proposed fuzzy time series model is outperforming all the competitive methods in the study. Furthermore, the proposed model forecasting shows a future decline tendency in WTi market crude oil prices (US$/BBL) after being at the record highest level, which is good news for the worldwide economy.

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.

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.

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.

The cross-talk between macrophages and tumor cells as a target for cancer treatment.

Macrophages represent an important component of the innate immune system. Under physiological conditions, macrophages, which are essential phagocytes, maintain a proinflammatory response and repair damaged tissue. However, these processes are often impaired upon tumorigenesis, in which tumor-associated macrophages (TAMs) protect and support the growth, proliferation, and invasion of tumor cells and promote suppression of antitumor immunity. TAM abundance is closely associated with poor outcome of cancer, with impediment of chemotherapy effectiveness and ultimately a dismal therapy response and inferior overall survival. Thus, cross-talk between cancer cells and TAMs is an important target for immune checkpoint therapies and metabolic interventions, spurring interest in it as a therapeutic vulnerability for both hematological cancers and solid tumors. Furthermore, targeting of this cross-talk has emerged as a promising strategy for cancer treatment with the antibody against CD47 protein, a critical macrophage checkpoint recognized as the "don't eat me" signal, as well as other metabolism-focused strategies. Therapies targeting CD47 constitute an important milestone in the advancement of anticancer research and have had promising effects on not only phagocytosis activation but also innate and adaptive immune system activation, effectively counteracting tumor cells' evasion of therapy as shown in the context of myeloid cancers. Targeting of CD47 signaling is only one of several possibilities to reverse the immunosuppressive and tumor-protective tumor environment with the aim of enhancing the antitumor response. Several preclinical studies identified signaling pathways that regulate the recruitment, polarization, or metabolism of TAMs. In this review, we summarize the current understanding of the role of macrophages in cancer progression and the mechanisms by which they communicate with tumor cells. Additionally, we dissect various therapeutic strategies developed to target macrophage-tumor cell cross-talk, including modulation of macrophage polarization, blockade of signaling pathways, and disruption of physical interactions between leukemia cells and macrophages. Finally, we highlight the challenges associated with tumor hypoxia and acidosis as barriers to effective cancer therapy and discuss opportunities for future research in this field.

Charting the Progress of Epilepsy Classification: Navigating a Shifting Landscape.

Epilepsy, a neurological disorder characterized by recurrent seizures, has witnessed a remarkable transformation in its classification paradigm, driven by advances in clinical understanding, neuroimaging, and molecular genetics. This narrative review navigates the dynamic landscape of epilepsy classification, offering insights into recent developments, challenges, and the promising horizon. Historically, epilepsy classification relied heavily on clinical observations, categorizing seizures based on their phenomenology and presumed etiology. However, the field has profoundly shifted from a symptom-based approach to a more refined, multidimensional system. One pivotal aspect of this evolution is the integration of neuroimaging techniques, particularly magnetic resonance imaging (MRI) and functional imaging modalities. These tools have unveiled the intricate neural networks implicated in epilepsy, facilitating the identification of distinct brain abnormalities and the categorization of epilepsy subtypes based on structural and functional findings. Furthermore, the role of genetics has become increasingly prominent in epilepsy classification. Genetic discoveries have not only unraveled the molecular underpinnings of various epileptic syndromes but have also provided valuable diagnostic and prognostic insights. This narrative review delves into the expanding realm of genetic testing and its impact on tailoring treatment strategies to individual patients. As the classification landscape evolves, there are accompanying challenges. The narrative review underscores the transformative potential of artificial intelligence and machine learning in epilepsy classification. These technologies hold promise in automating the analysis of complex neuroimaging and genetic data, offering enhanced accuracy and efficiency in epilepsy diagnosis and classification. In conclusion, navigating the shifting landscape of epilepsy classification is a journey marked by progress, complexity, and the prospect of improved patient care. We are charting a course toward more precise diagnoses and tailored treatments by embracing advanced neuroimaging, genetics, and innovative technologies. As the field continues to evolve, collaborative efforts and a holistic understanding of epilepsy's diverse manifestations will be instrumental in harnessing the full potential of this dynamic landscape.

Comparing Efficacy and Safety of Different Doses of Tirzepatide for the Treatment of Type 2 Diabetes Mellitus: A Meta-Analysis of Randomized Controlled Trials.

Our study assessed the efficacy and safety of the three primary tirzepatide (TZP) doses, 5 mg, 10 mg, and 15 mg using network meta-analysis to assess their relative impact on type 2 diabetes mellitus (T2DM) treatment. This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Two authors independently screened online databases, including PubMed, Cochrane Library, and Embase. We employed the keywords "Type 2 diabetes OR T2DM or diabetes" AND "Tirzepatide OR LY3298176 OR twincretin OR dual glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonist" AND "randomized controlled trial". The outcomes evaluated in this study comprised changes in hemoglobin (Hb)A1c levels from baseline (%), changes in weight from baseline (Kg), changes in fasting serum glucose from baseline (mg/dL), and occurrences of serious adverse events (SAE), adverse events (AE) and major adverse cardiovascular events (MACE). A total of eight studies met the inclusion criteria and were included in this meta-analysis. Our findings suggest that among the evaluated doses, TZP at 15 mg demonstrated superior effectiveness in reducing HbA1c, weight, and fasting serum glucose compared to doses of 10 mg and 5 mg. Notably, the reduction in HbA1c and weight showed a dose-dependent trend, with the 15 mg dose achieving the most substantial benefits. The safety analysis indicated that while serious adverse events and major adverse cardiovascular events (MACE) did not significantly differ among the three doses, the risk of overall adverse events was notably higher in the 10 mg and 15 mg TZP groups compared to the 5 mg group.

Complex Fermatean fuzzy extended TOPSIS method and its applications in decision making.

The fuzzy set has its own limitations due to the membership function only. The fuzzy set does not describe the negative aspects of an object. The Fermatean fuzzy set covers the negative aspects of an object. The complex Fermatean fuzzy set is the most effective tool for handling ambiguous and uncertain information. The aim of this research work is to develop new techniques for complex decision-making based on complex Fermatean fuzzy numbers. First, we construct different aggregation operators for complex Fermatean fuzzy numbers, using Einstein t-norms. We define a series of aggregation operators named complex Fermatean fuzzy Einstein weighted average aggregation (CFFEWAA), complex Fermatean fuzzy Einstein ordered weighted average aggregation (CFFEOWAA), and complex Fermatean fuzzy Einstein hybrid average aggregation (CFFEHAA). The fundamental properties of the proposed aggregation operators are discussed here. The proposed aggregation operators are applied to the decision-making technique with the help of the score functions. We also construct different algorithms based on different aggregation operators. The extended TOPSIS method is described for the decision-making problem. We apply the proposed extended TOPSIS method to MAGDM problem "selection of an English language instructor". We also compare the proposed models with the existing models.

A clinically useful and biologically informative genomic classifier for papillary thyroid cancer.

Clinical management of papillary thyroid cancer depends on estimations of prognosis. Standard care, which relies on prognostication based on clinicopathologic features, is inaccurate. We applied a machine learning algorithm (HighLifeR) to 502 cases annotated by The Cancer Genome Atlas Project to derive an accurate molecular prognostic classifier. Unsupervised analysis of the 82 genes that were most closely associated with recurrence after surgery enabled the identification of three unique molecular subtypes. One subtype had a high recurrence rate, an immunosuppressed microenvironment, and enrichment of the EZH2-HOTAIR pathway. Two other unique molecular subtypes with a lower rate of recurrence were identified, including one subtype with a paucity of BRAFV600E mutations and a high rate of RAS mutations. The genomic risk classifier, in addition to tumor size and lymph node status, enabled effective prognostication that outperformed the American Thyroid Association clinical risk stratification. The genomic classifier we derived can potentially be applied preoperatively to direct clinical decision-making. Distinct biological features of molecular subtypes also have implications regarding sensitivity to radioactive iodine, EZH2 inhibitors, and immune checkpoint inhibitors.

Detection of novel coronaviruses from dusky fruit bat (Penthetor lucasi) in Sarawak, Malaysian Borneo.

BACKGROUND: Sarawak has one of the highest diversity of fruit bats species (family Pteropodidae) in Malaysia, with 19 species described. Most coronavirus (CoV) studies have mainly focused on insectivorous bats, resulting in a lack of information on CoVs present in frugivorous bats. In addition, bat CoV surveillance activities are lacking in Malaysia. OBJECTIVES: Our study focuses on determining the presence of bat CoVs in dusky fruit bat (Penthetor lucasi). METHODS: Guano samples belonging to P. lucasi were collected from Wind Cave Nature Reserve. The samples were screened for the presence of CoVs using validated hemi-nested consensus RNA-dependent RNA polymerase consensus primers. RESULTS: The bat CoV positivity rate was 38.5% (n = 15/39), with the viruses belonging to two subgenera: Alphacoronavirus (α-CoV) and Betacoronavirus (ß-CoV). Phylogenetic analysis revealed that CoVs from 14 samples of P. lucasi belong to the genus α-CoV and may represent previously described genetic lineages in insectivorous bats in Wind Cave. However, only one sample of P. lucasi was detected with ß-CoV which is closely related to subgenus Nobecovirus, which is commonly seen in frugivorous bats. CONCLUSIONS: This study provides the first available data on CoVs circulating in P. lucasi.

Different responses of the rhizosphere microbiome to Verticillium dahliae infection in two cotton cultivars.

Verticillium wilt is a disastrous disease caused by Verticillium dahliae that severely damages the production of cotton in China. Even under homogeneous conditions, the same cotton cultivar facing V. dahliae tends to either stay healthy or become seriously ill and die. This binary outcome may be related to the interactions between microbiome assembly and plant health. Understanding how the rhizosphere microbiome responds to V. dahliae infection is vital to controlling Verticillium wilt through the manipulation of the microbiome. In this study, we evaluated the healthy and diseased rhizosphere microbiome of two upland cotton cultivars that are resistant to V. dahliae, Zhong 2 (resistant) and Xin 36 (susceptible), using 16S rRNA and ITS high-throughput sequencing. The results showed that the healthy rhizosphere of both resistant cultivar and susceptible cultivar had more unique bacterial ASVs than the diseased rhizosphere, whereas fewer unique fungal ASVs were found in the healthy rhizosphere of resistant cultivar. There were no significant differences in alpha diversity and beta diversity between the resistant cultivar and susceptible cultivar. In both resistant cultivar and susceptible cultivar, bacterial genera such as Pseudomonas and Acidobacteria bacterium LP6, and fungal genera such as Cephalotrichum and Mortierella were both highly enriched in the diseased rhizosphere, and Pseudomonas abundance in diseased rhizospheres was significantly higher than that in the healthy rhizosphere regardless of the cultivar type. However, cultivar and V. dahliae infection can cause composition changes in the rhizosphere bacterial and fungal communities, especially in the relative abundances of core microbiome members, which varied significantly, with different responses in the two cotton cultivars. Analysis of co-occurrence networks showed that resistant cultivar has a more complex network relationship than susceptible cultivar in the bacterial communities, and V. dahliae has a significant impact on the bacterial community structure. These findings will further broaden the understanding of plant-rhizosphere microbiome interactions and provide an integrative perspective on the cotton rhizosphere microbiome, which is beneficial to cotton health and production.

A new three way decision making technique for supplier selection in logistics service value Cocreation under intuitionistic double hierarchy linguistic term set.

In today's business world, choosing a logistics supplier is a critical factor for companies to improve operational efficiency and reduce business costs. With the development of market economy, it is very difficult for companies to choose a suitable logistics provider according to specific rules. Therefore, this study proposes a new three-way decision making (TWD) technique for supplier selection in logistics service value creation. For this, we first develop a new concept called intuitionistic double hierarchy linguistic term set (IDHLTSs) that can describe uncertainty and ambiguity in a more flexible way. Some Hamacher aggregation operators for collecting IDHLTSs information and its basic aspect are proposed. The unknown weight vector for decision experts and criteria is determined by using entropy measures. In addition, the conditional probability is determined using TOPSIS which makes the decision making process more rational. And the decision result is conducted according to minimum loss principle. Finally, an example of 3 PL supplier selection in the logistics service value co-creation environment and comparison is given to validate and demonstrate the effectiveness of the developed method.

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.

Solving the taxonomic identity of Hipposideros cineraceus sensu lato (Chiroptera: Hipposideridae) in the Thai-Malay Peninsula, with the description of a new species.

A new species of small Hipposideros in the bicolor group is described based on specimens from Thailand and Malaysia. It can be distinguished from other small Hipposideros in Southeast Asia by a combination of external, craniodental, and bacular morphology, as well as echolocation call frequency. The new species has a distinct rounded swelling on the internarial septum of the noseleaf, with a forearm length of 35.3-42.6 mm, greatest skull length of 15.94-17.90 mm, and a call frequency of maximum energy of 132.3-144.0 kHz. Although clearly different in morphology, the new species forms a sister clade with H. kunzi and H. bicolor in the phylogenetic trees based on mitochondrial DNA. In addition, this study reports echolocation and genetic data, with a confirmed record of H. einnaythu from Thailand for the first time. The new species most closely resembles H. einnaythu. However, it differs in the details of the noseleaf and craniodental morphology, and it has a genetic distance of 9.6% and 10.4% based on mitochondrial COI and ND2, respectively. It is currently documented from five localities: two in peninsular Thailand, at Hala Forest in Yala Province, and Phru To Daeng Swamp Forest in Narathiwat Province, one from peninsular Malaysia at Krau Wildlife Reserve in Pahang, and another two in Sabah, Malaysian Borneo at Gunung Kinabalu, and near Madai Caves. However, it is likely that many previous records of "H. cineraceus" from Borneo refer to this species. Most records of the species are from lowland evergreen rainforest, though one record from Sabah was at 1800m. The roosting sites for this new species are currently unknown. Future research with a combination of data such as genetics, echolocation and morphology would be necessary to further determine the species geographic distribution in Southeast Asia.

Implications of the COVID-19 pandemic on the shanghai, New York, and Pakistan stock exchanges.

This research aims to determine the impact of COVID-19 on the stock markets of Pakistan (Islamabad), China (Shanghai), and the United States of America (New York). These three stock markets were chosen to demonstrate the variation in the degree of influence based on varied times in which the respective nations were impacted by COVID-19. COVID-19, a pandemic virus, was still present in China in December 2020. The one-year timeline helps us understand the pattern of the effect on different stock markets that show onward to guide us to indicate that in this situation, the lack of economic movement (due to the lockdown) had a more negative effect on stock prices than the increase in the number of new confirmed cases of the COVID-19 virus. This study was carried out to assess the influence of COVID-19 on the financial sectors, including the stock market. The effects were assessed by employing the Autoregressive Distributed Lag Model (ARDL) to demonstrate correlations between three stock markets (Pakistan, Shanghai, and New York) and COVID-19 instances. The study's major goal is to demonstrate the differences in the three countries' levels of influence. We got empirical results and discovered that the confirmed cases had a detrimental influence on three stock exchanges. However, all three countries saw an increase in the number of recovery cases. The number of deaths was minor for Pakistan and China but had a detrimental impact on the New York Stock Exchange.