Transcranial direct current stimulation (tDCS) for neurological disability among subacute stroke survivors to improve multiple domains in health-related quality of life: Randomized controlled trial protocol.

OBJECTIVES: The primary goal of the current proposal is to fill the gaps in the literature by studying the effectiveness of transcranial direct current stimulation (tDCS) on lifestyle parameters, and physical, behavioral, and cognitive functions among stroke survivors, and understanding the factors that mediate the effects of various domains related to Health-related Quality of life (HRQoL) improvements. METHODS: Anticipated 64 volunteer subacute stroke survivors (>7 days to 3 months post stroke) aged 40-75 years with National Institutes of Health stroke scale (NIHSS) score of >10 and Mini-Mental State Examination (MMSE) score between 18 and 23 will be randomly assigned at a ratio of 1:1 to receive either: (1) 20 sessions of anodal tDCS or (2) sham tDCS in addition to conventional rehabilitation. Battery driven tDCS will be applied at 2 mA intensity to the dorsolateral prefrontal cortex and primary motor cortex for 20 minutes. The primary endpoints of study will be 36-Item Short Form Survey (SF-36) post intervention at 4 weeks. The secondary outcomes will include Stroke Specific Quality of Life Scale (SS_QOL), Montreal cognitive assessment (MCA), Beck Anxiety Inventory (BAI), Fugl-Meyer Assessment (FMA), 10 m walk test and Modified Barthel Activities of daily living (ADL) Index. At 0.05 level of significance, data normality, within group and between group actual differences will be analyzed with a moderate scope software. DISCUSSION: Our knowledge of this technique and its use is expanding daily as tDCS motor recovery studies-mostly single-center studies-in either single session or many sessions have been completed and shown positive results. The field is prepared for a multi-center, carefully planned, sham-controlled, double-blinded tDCS study to comprehensively examine its feasibility and effectiveness in enhancing outcomes in stroke population. CONCLUSION: The function of Transcranial Direct Current Stimulation in aiding stroke recuperation will be ascertained.

Combination use of human menstrual blood stem cell- derived exosomes and hyperbaric oxygen therapy, synergistically promote recovery after spinal cord injury in rats.

Traumatic spinal cord injury (TSCI) is one of the catastrophic events in the nervous system that leads to the loss of sensory and motor function of the spinal cord at the site of injury. Considering that several factors such as apoptosis, inflammation, and oxidative stress play a role in the spread of damage caused by trauma, therefore, the treatment should also be based on multifactorial approaches. Currently, we investigated the effects of human menstrual blood stem cells (MenSCs)-derived exosomes in combination with hyperbaric oxygen therapy (HBOT) in the recovery of TSCI in rats. Ninety male mature Sprague-Dawley (SD) rats were planned into five equal groups, including; control group, TSCI group, Exo group (underwent TSCI and received MenSCs -derived exosomes), HBOT group (underwent TSCI and received HBOT), and Exo+HBOT group (underwent TSCI and received MenSCs -derived exosomes plus HBOT). After the behavioral evaluation, tissue samples were obtained for stereological, immunohistochemical, biochemical, and molecular assessments. Our results showed that the numerical density of neurons, the concentrations of antioxidative biomarkers (CAT, GSH, and SOD), and neurological function scores were significantly greater in the treatments group than in the TSCI group, and these changes were more obvious in the Exo+HBOT ones (P<0.05). This is while the numerical densities of apoptotic cells and glial cells, the levels of an oxidative factor (MDA) and proinflammatory cytokines (IL-1ß and TNF-α) were considerably decreased in the treatment groups, specially the Exo+HBOT group, compared to the TSCI group (P<0.05). We conclude that the co-administration of exosomes derived from MenSCs and HBOT has more neuroprotective effects in animals with TSCI.

The emergence of highly resistant and hypervirulent Klebsiella pneumoniae CC14 clone in a tertiary hospital over 8 years.

BACKGROUND: Klebsiella pneumoniae is a major bacterial and opportunistic human pathogen, increasingly recognized as a healthcare burden globally. The convergence of resistance and virulence in K. pneumoniae strains has led to the formation of hypervirulent and multidrug-resistant strains with dual risk, limiting treatment options. K. pneumoniae clones are known to emerge locally and spread globally. Therefore, an understanding of the dynamics and evolution of the emerging strains in hospitals is warranted to prevent future outbreaks. METHODS: In this study, we conducted an in-depth genomic analysis on a large-scale collection of 328 multidrug-resistant (MDR) K. pneumoniae strains recovered from 239 patients from a single major hospital in the western coastal city of Jeddah in Saudi Arabia from 2014 through 2022. We employed a broad range of phylogenetic and phylodynamic methods to understand the evolution of the predominant clones on epidemiological time scales, virulence and resistance determinants, and their dynamics. We also integrated the genomic data with detailed electronic health record (EHR) data for the patients to understand the clinical implications of the resistance and virulence of different strains. RESULTS: We discovered a diverse population underlying the infections, with most strains belonging to Clonal Complex 14 (CC14) exhibiting dominance. Specifically, we observed the emergence and continuous expansion of strains belonging to the dominant ST2096 in the CC14 clade across hospital wards in recent years. These strains acquired resistance mutations against colistin and extended spectrum ß-lactamase (ESBL) and carbapenemase genes, namely blaOXA-48 and blaOXA-232, located on three distinct plasmids, on epidemiological time scales. Strains of ST2096 exhibited a high virulence level with the presence of the siderophore aerobactin (iuc) locus situated on the same mosaic plasmid as the ESBL gene. Integration of ST2096 with EHR data confirmed the significant link between colonization by ST2096 and the diagnosis of sepsis and elevated in-hospital mortality (p-value < 0.05). CONCLUSIONS: Overall, these results demonstrate the clinical significance of ST2096 clones and illustrate the rapid evolution of an emerging hypervirulent and MDR K. pneumoniae in a clinical setting.

Investigating Majhool date (Phoenix dactylifera) consumption effects on fasting blood glucose in animals and humans.

OBJECTIVES: Majhool date (Phoenix dactylifera), renowned for its premium taste and texture, is extensively consumed in the Islamic world, particularly during Ramadan. Despite its popularity, concerns persist regarding its potential to induce diabetes in non-patients. This study aims to explore the diabetogenic effects of prolonged Majhool date (Phoenix dactylifera) consumption, the widely used fruit in the Islamic world, through animal experiments and human clinical data. METHODS: Medjool dates were processed into an ethanolic extract for the animal experiment. Then, 21 Balb/c mice received varying doses of the extract for one month. The fasting blood glucose levels were analyzed at the beginning and after one month of consumption of the Majhool date extract. For the clinical study, 387 healthy participants were recruited, with fasting blood glucose levels assessed before and after Ramadan, a period of heightened Majhool date consumption. RESULTS: all groups of the experimental animals exhibited a significant (p<0.05) weight increase after Majhool date consumption, while no significant (p>0.05) alteration in fasting blood glucose levels among groups. In addition, it was found that fasting blood glucose levels remained statistically unchanged (p>0.05) after heightened Majhool date consumption among humans. CONCLUSIONS: The study challenges the belief that Majhool date induces diabetes, supported by both animal and human data. Findings suggest that Majhool date consumption, even at higher doses, does not induce diabetes. Further investigations could explore the impact of other date varieties on the fasting blood glucose levels.

Visual outcome of methanol toxic optic neuropathy after erythropoietin treatment in Riyadh, Saudi Arabia.

PURPOSE: The purpose of this study is to evaluate the visual response of methanol-induced optic neuropathy to management with erythropoietin (EPO) along with conventional therapy. METHODS: This retrospective case series examines the ophthalmological data of patients diagnosed with methanol-induced optic neuropathy between 2020 and 2021 at two centers, Riyadh, Saudi Arabia. Patients' characteristics and the results of initial and final ophthalmological examinations were documented and compared between patients who received EPO in addition to conventional management and those who received only conventional management. RESULTS: A total of nine cases were reviewed, of which eight (88.9%) were males and one was female (11.1%). The mean age was 37.7 years. At presentation, funduscopic examination revealed optic disc edema in four eyes (two patients), and 14 eyes had normal appearance (seven patients). Among the nine patients who received conventional management, 5 (55.6%) additionally received intravenous EPO during the treatment course. There was no clinically or statistically significant difference in terms of visual outcome between the two groups. The mean visual acuity at the final presentation was 1.32 ± 0.79 logarithm of the minimum angle of resolution (LogMAR) in the EPO group and 1.36 ± 0.85 LogMAR in the non-EPO group. Optical coherence tomography indicated that the EPO group had an average retinal nerve fiber layer thickness of 48.13µm (±6.2), at the final assessment. CONCLUSION: Managing the visual impairments in individuals with methanol-induced optic neuropathy using intravenous EPO resulted in similar final visual outcomes compared to conventional management.

Machine learning and statistical physics modeling of tetracycline adsorption using activated carbon derived from Cynometra ramiflora fruit biomass.

The current investigation reports the usage of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), the two recognized machine learning techniques in modelling tetracycline (TC) adsorption onto Cynometra ramiflora fruit biomass derived activated carbon (AC). Many characterization methods utilized, confirmed the porous structure of synthesized AC. ANN and ANFIS models utilized pH, dose, initial TC concentration, mixing speed, time duration, and temperature as input parameters, whereas TC removal percentage was designated as the output parameter. The optimized configuration for the ANN model was determined as 6-8-1, while the ANFIS model employed trimf input and linear output membership functions. The obtained results showed a strong correlation, indicated by high R2 values (ANNR2: 0.9939 & ANFISR2: 0.9906) and low RMSE values (ANNRMSE: 0.0393 & ANFISRMSE: 0.0503). Apart from traditional isotherms, the dataset was fitted to statistical physics models wherein, the double-layer with a single energy satisfactorily explained the physisorption mechanism of TC adsorption. The sorption energy was 21.06 kJ/mol, and the number of TC moieties bound per site (n) was found to be 0.42, conclusive of parallel binding of TC molecules to the adsorbent surface. The adsorption capacity at saturation (Qsat) was estimated to be 466.86 mg/g - appreciably more than previously reported values. These findings collectively demonstrate that the AC derived from C. ramiflora fruit holds great potential for efficient removal of TC from a given system, and machine learning approaches can effectively model the adsorption processes.

Evaluation of Habenaria aitchisonii Reichb. for antioxidant, anti-inflammatory, and antinociceptive effects with in vivo and in silico approaches.

Habenaria aitchisonii Reichb was analyzed in this research, including its chemical composition and its in vitro antioxidant, anti-inflammatory, acute oral toxicity, and antinociceptive activity. The chloroform and ethyl acetate fractions were found to be the most powerful based on in vitro antioxidant, anti-inflammatory, and analgesic assays. The acute oral toxicity of the crude methanolic extract was determined before in vivo studies. The acetic acid and formalin tests were used to measure the antinociceptive effect, and the potential mechanisms involved in antinociception were explored. The carrageenan-induced paw edema test was used to examine the immediate anti-inflammatory effect, and many phlogistic agents were used to determine the specific mechanism. Furthermore, for ex vivo activities, the mice were sacrificed, the forebrain was isolated, and the antioxidant levels of glutathione (GSH), superoxide dismutase (SOD), thiobarbituric acid reactive substances (TBARS) and catalase (CAT) were estimated using a UV spectrophotometer. No toxicity was seen at oral dosages up to 3,000 mg/kg. The antinociceptive impact was much higher than the standard drug. Both the inflammatory and neurogenic phases of the formalin experiment revealed an analgesic effect in the chloroform and ethyl acetate fractions. In carrageenan anti-inflammatory assays, the chloroform fraction (Ha.Chf) was the most potent fraction. We further studied the GC-MS of crude plant extract and found a total of 18 compounds. In the anti-inflammatory mechanism, it was observed that the Ha.Chf inhibits the COX-2 as well as 5-LOX pathways. The results exhibited that this species is a good source of phytocomponents like germacrone, which can be employed as a sustainable and natural therapeutic agent, supporting its traditional use in folk medicine for inflammatory conditions and pain.

Epidemiology, clinical presentation, and outcome of mpox: A study of 381 cases in Saudi Arabia.

Objectives: There are limited data on the clinical and epidemiological aspects of mpox in Saudi Arabia. This study investigates the characteristics of Mpox cases from May to September 2023. Methods: A total of 381 cases of Mpox were included in this study, diagnosed based on a combination of clinical symptoms and laboratory testing. Results: The majority of mpox cases were males (91.1%), with a mean age of (±SD) of 32.4 (±8.3) years; 356 (93.4%) did not report travel, 277 (72.7%) denied engaging in extra-marital sex, and 379 (99.5%) were not linked to secondary cases. Fever was reported in 371 (97.4%), whereas headache was present in 314 (82.4%). Cough (1.3%) and conjunctivitis (0.5%) were rare. The most commonly affected areas in terms of lesions were the palms and soles (297 cases, 78%), followed by the genitals (206 cases, 54%), face (198 cases, 52%), and mouth (160 cases, 42%). Of the 1325 identified contacts, 1134 (85.5%) were hospital contacts, and 191 (14.5%) were community contacts, and 393 (29.6%) were high-risk contact. Of the high-risk contacts, 284 (72.3%) accepted post-exposure vaccination. The genotyped samples were all subclade IIb (formerly the West Africa clade). Conclusions: This study provides valuable insights into mpox characteristics in Saudi Arabia. The genome of monkeypox virus belonged to subclade IIb of the West Africa clade. Further analysis of the global tree sublineage is needed.

Lanthanum and Indium intermetallics nanomaterial for thermal photovoltaic applications - A full potential study.

In this work Full Potential study performed on Lanthanum compounds to analyze its photovoltaic properties. Five different combinations of Lanthanum and Indium with phosphorus are chosen in this study are La3P, La2InP, LaIn2P, LaP and InP. The optical, structural, thermoelectric, thermal, and electronic properties of all the above-mentioned compounds are analyzed using Density Functional Theory (DFT) applied in the WIEN2k software. Based on the analysis of electronic properties is concluded that La3P, LaP, La2InP and LaIn2P are conductors whereas InP is semiconductor (direct band gap) with band gap (energy) value 0.39 eV. The optical properties analysis shows these materials have desirable properties in the near UV or in the UV region. The low value of Gibbs energy indicates high thermodynamic stability. Power factor values for La2InP, LaP, InP and La3P are found to be in agreement with existing thermoelectric material, rendering them as potential thermal photovoltaic materials.

Predicting mechanical neck pain intensity in computer professionals using machine learning: identification and correlation of key features.

Introduction: Mechanical neck pain has become prevalent among computer professionals possibly because of prolonged computer use. This study aimed to investigate the relationship between neck pain intensity, anthropometric metrics, cervical range of motion, and related disabilities using advanced machine learning techniques. Method: This study involved 75 computer professionals, comprising 27 men and 48 women, aged between 25 and 44 years, all of whom reported neck pain following extended computer sessions. The study utilized various tools, including the visual analog scale (VAS) for pain measurement, anthropometric tools for body metrics, a Universal Goniometer for cervical ROM, and the Neck Disability Index (NDI). For data analysis, the study employed SPSS (v16.0) for basic statistics and a suite of machine-learning algorithms to discern feature importance. The capability of the kNN algorithm is evaluated using its confusion matrix. Results: The "NDI Score (%)" consistently emerged as the most significant feature across various algorithms, while metrics like age and computer usage hours varied in their rankings. Anthropometric results, such as BMI and body circumference, did not maintain consistent ranks across algorithms. The confusion matrix notably demonstrated its classification process for different VAS scores (mild, moderate, and severe). The findings indicated that 56% of the pain intensity, as measured by the VAS, could be accurately predicted by the dataset. Discussion: Machine learning clarifies the system dynamics of neck pain among computer professionals and highlights the need for different algorithms to gain a comprehensive understanding. Such insights pave the way for creating tailored ergonomic solutions and health campaigns for this population.

Microplastics and associated polycyclic aromatic hydrocarbons in surface water and sediment of the Bay of Bengal coastal area, India: sources, pathway and ecological risk.

In the aquatic environment around the world, microplastic contamination has been a common and ongoing issue. Particularly, the ability of microplastics to absorb persistent organic pollutants (POPs) and then transmit these POPs to aquatic creatures has attracted a lot of interest. A stereomicroscope was used to detect the size, shape, and color of the microplastics (MPs), and Fourier Transform Infrared (FTIR) spectroscopy was used to identify the polymer composition of the MPs. To address MP transit, destiny, and mitigation, a study of MP pollution coastal areas is required. In the current study, MP pollution in the collected sample from upper layer of water and sediment of the Digha and Puri beaches along the coast of BOB was evaluated. The average concentration with SD of MPs observed in water was 5.3 ± 1.8 items/L whereas, in sediments, it was 173.4 ± 40.1 items/kg at Digha beach. The mean MPs abundance in the Puri beach was 6.4 ± 1.7 items/L in the water and 190.4 ± 28.0 items/kg in the sediments. The investigated total 16-PAHs concentrations were 164.7 ng/g, 121.9 ng/g, 73.6 ng/g, and 101.3 ng/g on the MPs surface of foam, fragment, fibers, and film respectively in the studied MPs sample. Smaller than 1000 µm size of MPs are distributed in the largest concentration. Fibers, films, fragments, and foam were the most common shapes of MPs. The molecular structure of MPs in water and sediment samples was analysed i.e., polyesters (PEs), polypropylene (PP), polyethylene (PE), polymethyl methacrylate (PMMA), polystyrene (PS), polyamide (PA), polycarbonates (PC), and polyurethane (PU). The obtained result offers an accurate assessment of the PLI, and the investigated polymer facilitates determining the polymer hazard levels, which emphasizes the risk associated with it.

4-Carboxyphenyl as efficient donor group in nano Zn-Porphyrin for dye sensitized solar cells.

Dye-sensitized solar cells, represent the alternate technology in solar research due to their cost effective, easy fabrication processes, higher efficiencies, and design flexibility. In this research, dual donor group modified zinc porphyrin dyes, have been synthesized for DSSCs. The complexes of zinc porphyrin functioned as acceptor or attaching groups within each mesophenyl ring and carboxylic acid. These complexes exhibited diverse alkyl substituents and sizable electron-donating substituents, contributing to their varied chemical structures and potential applications. The dual Donor-π bridge -Acceptor group sensitizers, Zn[5,15-diphenylcarbazole-10,20-(4-carboxyphenyl) Porphyrin] (KSR-1) and Zn [5,15-thiadiazole-10,20-(4-carboxyphenyl) Porphyrin] (KSR-2) have been synthesized and adopted for DSSCs implementation. The molar absorption coefficients (ε) of KSR-2 and KSR-1 Soret bands were 0.56 x 105 mol/L/cm and 0.47 x 105 mol/L/cm, respectively. The Q bands of the KSR-1 and KSR-2 dyes were 1.10 x 105 mol/L/cm and 1.0 x 105 mol/L/cm, respectively and the molar absorption coefficient of the KSR-1 dye was greater when compared to the KSR-2 dye. The molar absorption coefficient of 0.71 x 105 mol/L/cm was visible in the KSR -1 Q-band. DFT calculations and the electrochemical characteristics of the KSR-1 and KSR-2 dyes have been studied and discussed. The exploration involved in investigating the photophysical properties and photovoltaic performance which were affected by varying the length and number of the donor entities. The wall-plug efficiency of the KSR-1 based solar panel was Voc = 0.68 V, Jsc = 8.94 mA/m2, FF = 56 and Efficiency (µ) = 3.44%. The wall-plug efficiency of the KSR-2 based solar panel was Voc = 0.63 V, Jsc = 5.42 mA/m2, FF = 53 and Efficiency (µ) = 1.83%.

Computer-aided drug discovery of c-Abl kinase inhibitors from plant compounds against chronic myeloid leukemia.

Chronic myeloid leukemia (CML) is a hematological malignancy characterized by the neoplastic transformation of hematopoietic stem cells, driven by the Philadelphia (Ph) chromosome resulting from a translocation between chromosomes 9 and 22. This Ph chromosome harbors the breakpoint cluster region (BCR) and the Abelson (ABL) oncogene (BCR-ABL1) which have a constitutive tyrosine kinase activity. However, the tyrosine kinase activity of BCR-ABL1 have been identified as a key player in CML initiation and maintenance through c-Abl kinase. Despite advancements in tyrosine kinase inhibitors, challenges such as efficacy, safety concerns, and recurring drug resistance persist. This study aims to discover potential c-Abl kinase inhibitors from plant compounds with anti-leukemic properties, employing drug-likeness assessment, molecular docking, in silico pharmacokinetics (ADMET) screening, density function theory (DFT), and molecular dynamics simulations (MDS). Out of 58 screened compounds for drug-likeness, 44 were docked against c-Abl kinase. The top hit compound (isovitexin) and nilotinib (control drug) were subjected to rigorous analyses, including ADMET profiling, DFT evaluation, and MDS for 100 ns. Isovitexin demonstrated a notable binding affinity (-15.492 kcal/mol), closely comparable to nilotinib (-16.826 kcal/mol), showcasing a similar binding pose and superior structural stability and reactivity. While these findings suggest isovitexin as a potential c-Abl kinase inhibitor, further validation through urgent in vitro and in vivo experiments is imperative. This research holds promise for providing an alternative avenue to address existing CML treatment and management challenges.Communicated by Ramaswamy H. Sarma.

Prevalence of depression in postmyocardial infarction patients in a tertiary care center in Riyadh.

BACKGROUND: The coexistence of coronary artery disease (CAD) and depression is a growing concern, as both conditions lead to disability. Although depression is more prevalent in CAD patients than in the general population and has been associated with adverse cardiac outcomes, the underlying mechanisms linking depression and CAD are not yet fully understood. This study aims to assess the prevalence of depression in postmyocardial infarction (MI) patients as baseline data in Saudi Arabia. MATERIALS AND METHODS: A cross-sectional study was conducted at King Saud Medical City, Riyadh. The study population included male and female patients who had survived MI from January 2022 to June 2022. A sample size of 323 patients was initially planned, but only 249 patients could be included on account of exclusions. The patients underwent screening for depression using Patient Health Questionnaire-2 (PHQ-2), and those who were positive on screening were further assessed using the PHQ-9 according to DSM-5 criteria. Sociodemographic data, comorbidities, and previous cardiac interventions were collected from medical records. RESULTS: The mean age of the study participants was 57.15 years, and majority (76.6%) were males. The prevalence of previously diagnosed depression was 9.2%, and 5.2% of patients reported using antidepressant medication. According to the PHQ-9 scores, 33.33% had depression, 9% had moderate depression, and 2.4% had severe depression. There were significant associations between the severity of depression and previous CAD (P < 0.05), previous coronary artery bypass graft surgery (P < 0.05), and heart failure (P < 0.05). CONCLUSION: This study reveals a high prevalence of depression in post-MI patients at King Saud Medical City. The findings highlight the need for comprehensive management of depression in this population to improve outcomes. Further research into the underlying mechanisms linking depression and CAD to develop effective interventions is required.

An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images.

Biomedical image analysis plays a crucial role in enabling high-performing imaging and various clinical applications. For the proper diagnosis of blood diseases related to red blood cells, red blood cells must be accurately identified and categorized. Manual analysis is time-consuming and prone to mistakes. Analyzing multi-label samples, which contain clusters of cells, is challenging due to difficulties in separating individual cells, such as touching or overlapping cells. High-performance biomedical imaging and several medical applications are made possible by advanced biosensors. We develop an intelligent neural network model that can automatically identify and categorize red blood cells from microscopic medical images using region-based convolutional neural networks (RCNN) and cutting-edge biosensors. Our model successfully navigates obstacles like touching or overlapping cells and accurately recognizes various blood structures. Additionally, we utilized data augmentation as a pre-processing method on microscopic images to enhance the model's computational efficiency and expand the sample size. To refine the data and eliminate noise from the dataset, we utilized the Radial Gradient Index filtering algorithm for imaging data equalization. We exhibit improved detection accuracy and a reduced model loss rate when using medical imagery datasets to apply our proposed model in comparison to existing ResNet and GoogleNet models. Our model precisely detected red blood cells in a collection of medical images with 99% training accuracy and 91.21% testing accuracy. Our proposed model outperformed earlier models like ResNet-50 and GoogleNet by 10-15%. Our results demonstrated that Artificial intelligence (AI)-assisted automated red blood cell detection has the potential to revolutionize and speed up blood cell analysis, minimizing human error and enabling early illness diagnosis.

Oncogenic Potential of Replication Factor C Subunit 4: Correlations with Tumor Progression and Assessment of Potential Inhibitors.

Replication Factor C Subunit 4 (RFC4), an oncogene implicated in many human cancers, has yet to be extensively studied in many cancer types to determine its expression patterns and tumor tissue function. Various bioinformatics tools were used to analyze RFC4 as a potential oncogene and therapeutic target across many cancers. We first examined RFC4 expression levels in several human tumor types to determine relationships with tumor grade, stage, metastasis, and patient survival. We also examined RFC4's genetic changes, epigenetic methylation, and effect on tumor microenvironment (TME) immune cell infiltration. We also analyzed RFC4's connections with immunological checkpoints to identify potential molecular pathways involved in carcinogenesis. Our findings show that RFC4 is upregulated in several tumor types and associated with poor prognoses in many human cancers. This study shows that RFC4 significantly affects the tumor immunological microenvironment, specifically immune cell populations. Finally, we screened for RFC4-inhibiting pharmacological compounds with anti-cancer potential. This study fully elucidates RFC4's carcinogenic activities, emphasizing its potential as a prognostic biomarker and a target for anti-cancer therapy.

Dementia and Multimorbidity Trends in Al-Baha, Saudi Arabia: An Analytical Retrospective Study Using Records-Based Data.

BACKGROUND: The prevalence of dementia is escalating significantly, posing a substantial societal burden. Currently, there exists a dearth of comprehensive health data about dementia patients in Saudi Arabia, particularly within Al-Baha City. METHODS: A retrospective case-series study was undertaken to ascertain the prevalence of dementia within the populace of the Al-Baha region, Kingdom of Saudi Arabia. This investigation utilized hospital-based records encompassing individuals exhibiting symptoms or diagnosed with dementia and its related forms across the Al-Baha region. Furthermore, the study aimed to evaluate the burden of comorbidities among dementia patients and document the pharmacological therapeutic interventions administered to manage dementia and its associated concurrent health conditions. RESULTS: Our investigation explored the prevalence rates of various forms of dementia and the accompanying comorbidities among affected individuals. The study spanned from August 2020 to August 2023. Our study encompassed 407 patients diagnosed with Alzheimer's disease (AD), Parkinson's disease, vascular dementia (VaD), or other forms of dementia who were either admitted to or attended tertiary hospitals in Al-Baha. Assessment of the comorbidity burden was conducted using the Charlson Comorbidity Index (CCI). Our findings revealed that among these patients, 13.3% presented with AD, 23.6% with VaD, 33.4% with Parkinson's disease, 15.75% with amnesia, and 14.0% with other types of dementia. The spectrum of comorbidities observed among dementia patients encompassed various conditions, with diabetes mellitus emerging as the predominant comorbidity (19.1%), followed by hypertension (16.4%). Additionally, manifestations of depression were noted in 14% of patients, while 9.82% suffered from paralysis. Chronic conditions such as cancer, chronic obstructive pulmonary disorder (COPD), and cervical spondylosis were also observed among individuals afflicted with dementia and its varied forms. Statistically significant correlations were established between gender, age, nationality, comorbidities, and the prevalence of dementia. Therapeutic interventions in the form of pharmacological treatments were prescribed for dementia patients with comorbidities. Commonly administered medications included Amlod (6.3%), Amlodipine (6.6%), Amlor (5.8%), Aspirin (10.5%), chemotherapeutic drugs (4.4%), Glipizide (8.5%), Lantus (11.3%), Levodopa (23.5%), Metformin (7.8%), acetylcholinesterase inhibitors (6.8%), and Pulmicort (7.86%). These medications aimed to alleviate symptoms associated with dementia and its accompanying comorbidities. CONCLUSIONS: Our investigation underscores the substantial burden of comorbidities experienced by dementia patients. These findings offer crucial insights into the overall health status of individuals grappling with dementia, serving as a catalyst for increased awareness among clinicians and policymakers. Such awareness can drive improvements in medical care and support frameworks tailored to the specific needs of dementia patients.

ChestCovidNet: An Effective DL-based Approach for COVID-19, Lung Opacity, and Pneumonia Detection Using Chest Radiographs Images.

Currently used lung disease screening tools are expensive in terms of money and time. Therefore, chest radiograph images (CRIs) are employed for prompt and accurate COVID-19 identification. Recently, many researchers have applied Deep learning (DL) based models to detect COVID-19 automatically. However, their model could have been more computationally expensive and less robust, i.e., its performance degrades when evaluated on other datasets. This study proposes a trustworthy, robust, and lightweight network (ChestCovidNet) that can detect COVID-19 by examining various CRIs datasets. The ChestCovidNet model has only 11 learned layers, eight convolutional (Conv) layers, and three fully connected (FC) layers. The framework employs both the Conv and group Conv layers, Leaky Relu activation function, shufflenet unit, Conv kernels of 3×3 and 1×1 to extract features at different scales, and two normalization procedures that are cross-channel normalization and batch normalization. We used 9013 CRIs for training whereas 3863 CRIs for testing the proposed ChestCovidNet approach. Furthermore, we compared the classification results of the proposed framework with hybrid methods in which we employed DL frameworks for feature extraction and support vector machines (SVM) for classification. The study's findings demonstrated that the embedded low-power ChestCovidNet model worked well and achieved a classification accuracy of 98.12% and recall, F1-score, and precision of 95.75%.

Minimally invasive sacroiliac joint fusion using triangular titanium implants versus nonsurgical management for sacroiliac joint dysfunction: a systematic review and meta-analysis.

BACKGROUND: Minimally invasive sacroiliac joint (MISIJ) fusion is a surgical option to relieve SIJ pain. The aim of this systematic review and meta-analysis was to compare MISIJ fusion with triangular titanium implants (TTI) to nonoperative management of SIJ dysfunction. METHODS: We searched MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. We included prospective clinical trials that compared MISIJ fusion to nonoperative management in individuals with chronic low back pain attributed to SIJ dysfunction. We evaluated pain on visual analogue scale, Oswestry Disability Index (ODI) score, health-related quality of life (HRQoL) using the 36-Item Short Form Health Survey (SF-36) physical component (PCS) and mental component summary (MCS) scores, patient satisfaction, and adverse events. RESULTS: A total of 8 articles representing 3 trials that enrolled 423 participants were deemed eligible. There was a significant reduction in pain score with MISIJ fusion compared with nonoperative management (standardized mean difference [SMD] -1.71, 95% confidence interval [CI] -2.03 to -1.39). Similarly, ODI scores (SMD -1.03, 95% CI -1.24 to -0.81), SF-36 PCS scores (SMD 1.01, 95% CI 0.83 to 1.19), SF-36 MCS scores (SMD 0.72, 95% CI 0.54 to 0.9), and patient satisfaction (odds ratio 6.87, 95% CI 3.73 to 12.64) were significantly improved with MISIJ fusion. No significant difference was found between the 2 groups with respect to adverse events (SMD -0.03, 95% CI -0.28 to 0.23). CONCLUSION: Our analysis showed that MISIJ fusion with TTI shows a clinically important and statistically significant improvement in pain, disability score, HRQoL, and patient satisfaction with a similar adverse event profile to nonoperative management in patients with chronic low back pain attributed to SIJ dysfunction.

Key dependent information confidentiality scheme based on deoxyribonucleic acid (DNA) and circular shifting.

In this era of advanced information technology, the exploration and development of novel mechanisms to ensure information confidentiality have consistently captivated the attention of upcoming researchers. In this article, we present a pioneering approach that combines DNA sequencing with a four-dimensional (4D) hyperchaotic map to bolster the security of digital information. Our primary focus is on the design of a robust and secure scheme for encrypting color images, leveraging DNA cryptography and hyperchaos. By extracting three distinct DNA sequences, we generate encryption keys through the integration of DNA computing and 4D hyperchaotic maps. Notably, these keys are intricately linked to the plaintext and vary with any alterations in the input. Consequently, the proposed encryption method stands resilient against an array of potential cryptographic attacks. To gauge the algorithm's security, we subject it to rigorous standard statistical analysis. Our findings underscore the efficiency and robustness of the proposed framework, establishing its potential for facilitating secure communication.