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Abstract Background Pathogenic Escherichia coli strains produce neonatal septicemia after colonizing the neonatal gut. While the probiotic Lactobacillus rhamnosus GG (LGG) effectively reduces neonatal sepsis, LGG’s effects on the neonatal intestinal microbiota alterations and inflammation triggered by E. coli are incompletely understood. We hypothesized that LGG significantly modulates the specific neonatal gut microbial populations changes and the inflammatory response elicited by the enteral introduction of septicemia-producing E. coli. To test this hypothesis, newborn rats were pretreated orally with LGG or placebo prior to infection with the neonatal E. coli septicemia clinical isolate SCB34. Amplicon 16S rRNA gene sequencing was performed on intestinal samples. Intestinal injury and expression of inflammatory mediators and apoptosis were determined. Results Alpha diversity of gut microbiota was greater in SCB34-infected pups in comparison to sham-infected pups, these changes were not modified by LGG pretreatment. Beta diversity analyses also showed differences between SCB34-infected vs. uninfected pups. LGG pretreatment before SCB34 infection did not result in significant beta diversity changes compared to placebo. Moreover, individual genera and species abundance analyses by linear discriminant analysis effect size (LEfSe) showed significant changes in Gram-negative, Gram-positive, and anaerobic populations resulting from LGG pretreatment and SCB34 infection. LGG significantly suppressed the expression of inflammatory cytokines but did not attenuate SCB34-induced apoptosis or histologic injury. Conclusions LGG modulates clinically significant microbiota features and inflammation triggered by pathogenic E. coli intestinal infection shortly after birth. This new knowledge can potentially be harnessed to design novel interventions against gut-derived neonatal sepsis.

Abstract An antenna array having a size of 45 $$\:\times\:$$ 40 cm2 (5.7 $$\:\times\:$$ 5 $$\:{\lambda\:}_{0}$$ 2) and consisting of four pairs of printed U-shaped dipoles positioned above a metal reflector, for 5G Sub-6 GHz base station applications, is designed and tested. The array consists of eight excitation ports, one port for each dipole. Four parasitic square patches are etched on the bottom side of the dipole arms for producing radiations in 2.2 GHz and 3.8 GHz bands. The size of the reflector and height of the dipoles are optimized in order to enhance antenna gain up to 11.5 dB at 2.2 GHz and 14.5 dB at 3.8 GHz. Beam steering up to 20 $$\:^\circ\:$$ is achieved, using phase shifted simultaneous excitation of different ports. The proposed antenna array not only fulfills 5G base station requirements but is also simple and compact as it only requires eight ports to achieve dual-band, high-gain and beam steering operation in a single design. It also offers a unique feature of dual-sector coverage per panel, which results in an increased coverage capacity of the base station without increasing the system resources.

Abstract This paper explores the class C G $\mathcal{C}_{G}$ , consisting of functions g that satisfy a specific subordination relationship with Gregory coefficients in the open unit disk E. By applying certain conditions to related coefficient functionals, we establish sharp estimates for the first five coefficients of these functions. Additionally, we derive bounds for the second and third Hankel determinants of functions in C G $\mathcal{C}_{G}$ , providing further insight into the class’s properties. Our study also investigates the logarithmic coefficients of log ( g ( t ) t ) $\log \left ( \frac{g(t)}{t}\right ) $ and the inverse coefficients of the inverse functions ( g − 1 ) $(g^{-1})$ within the same class.

Rapeseed (Brassica napus L.) is the second most widely grown premium oilseed crop globally, mainly for its vegetable oil and protein meal. One of the main goals of breeders is producing high-yield rapeseed cultivars with sustainable production to meet the requirements of the fast-growing population. Besides the pod number, seeds per silique (SS), and thousand-seed weight (TSW), the ovule number (ON) is a decisive yield determining factor of individual plants and the final seed yield. In recent years, tremendous efforts have been made to dissect the genetic and molecular basis of these complex traits, but relatively few genes or loci controlling these traits have been reported thus far. This review highlights the updated information on the hormonal and molecular basis of ON and development in model plants (Arabidopsis thaliana). It also presents what is known about the hormonal, molecular, and genetic mechanism of ovule development and number, and bridges our understanding between the model plant species (A. thaliana) and cultivated species (B. napus). This report will open new pathways for primary and applied research in plant biology and benefit rapeseed breeding programs. This synopsis will stimulate research interest to further understand ovule number determination, its role in yield improvement, and its possible utilization in breeding programs.

Trigeminal neuralgia (TN) associated with brainstem lesions as revealed by Magnetic resonance imaging (MRI), is a rare condition. The MRI often shows a distinctive single pontine in cases of TN (SPL-TN). While the significance of this MRI finding remains unclear, various case reports suggest a potential link to chronic injury in the pontine pathways of the trigeminal nerve. In this report, we present the case of a 42-year-old female who was referred for TN that is refractory to medical treatment with an ipsilateral MRI lesion over the pons who had an excellent response to a trigeminal nerve bock, shedding light on the intriguing interplay between TN and pontine lesions.

Abstract In the era of the green revolution, the manufacturing sector faces intense pressure to adopt eco-friendly production practices, which can be achieved by implementing an ESG (environmental, social, governance) framework. ESG can enhance firms' performance and play an incredible role in promoting sustainable development practices. The current study conducts a bibliometric analysis to examine the ESG performance in the manufacturing sector to facilitate a green revolution. It extracted 314 documents from the Scopus and Web of Sciences databases from 1994 to 2023. The average age of the document is 34.78, and it was from 159 sources and involved 896 total authors. The study analyzed the prior literature and supporting theories, current conceptual development, and future research themes. The evolution process of ESG started in the 1960s and is still theoretically and empirically evolving to meet future challenges. Results show an increasing trend in ESG-related publications during the sample period. Journal of Cleaner Production is the primary source of publication, and China is the most prominent country in terms of correspondence and institutional affiliation. The continent-specific analysis indicates that almost 75% of the research was conducted in Asia and Europe. Four research themes were identified through co-occurrence analysis: (1) framework for social, financial, and environmental performance, (2) sustainable models for innovative environmental investment, (3) manufacturing practices and governance issues, and (4) application for eco-friendly production and economic growth. The findings suggest that the manufacturing sector can lead toward a more sustainable and equitable future through a steadfast commitment to ESG performance that can lead to a global green revolution. Further, it outlines the potential future directions integrated into the discourse on sustainable manufacturing.

Abstract Background Type-D personality is an established predisposing factor for various diseases. Type-D traits have been shown to pose a 26% increased risk of coronary artery disease after controlling for other confounding factors. Significant associations have been reported between type-D personality traits and dyslipidaemia, impaired endothelial function, coronary heart disease (CAD), acute myocardial infarction, and other adverse cardiovascular events. Objective To assess the association between type-D personality and left-ventricular adverse remodelling in patients treated with percutaneous coronary intervention following index ST-segment elevation myocardial infarction. Methods All patients hospitalized and treated with percutaneous coronary intervention (PCI) after their index ST-segment elevation myocardial infarction (STEMI) between 1 January 2022 to 31 December 2023 were prospectively enrolled. Type-D personality traits in the study population were determined at baseline using type-D Scale-14 (DS14) instrument, whereas any positive change in left ventricular end diastolic volume (LVEDV) ≥ 20% at follow up period of 12-months from baseline was defined as left-ventricular adverse remodelling (LVAR). Univariate and multivariate analysis was done to establish the independent predictors of LVAR. The area under receiver-operating characteristic curve (AUROC) was employed to assess the sensitivity and specificity of the identified independent predictors. Results A total of 124 patients were enrolled in the study. The mean age of the study population was 67 ± 10 years and the overall incidence of LVAR was found to be 25%. Multivariate regression analysis revealed that type-D personality is a significant independent predictor of LVAR $$\:(OR:2.51;95\%CI:1.16-5.77;p=0.03)$$ apart from the already established independent predictors Killip Class $$\:\:(OR:7.30;95\%CI:3.03-17.55;p

Objective: To explore the effect of geraniol on cyclophosphamide- induced cardiotoxicity. Methods: Mice were divided into five groups: the control group, the cyclophosphamide group (200 mg/kg cyclophosphamide, i.p. on day 7), the group treated with geraniol 100 and 200 mg/kg from day 1 to day 14, along with a single dose of cyclophosphamide on day 7, and the geraniol alone group (200 mg/kg geraniol from day 1 to day 14). At the end of the study, animals were sacrificed, and blood and heart were collected and analyzed for biochemical, histopathological, and immunohistochemical changes. Results: Treatment with 200 mg/kg geraniol significantly reduced the levels of cardiac injury markers, malondialdehyde, and inflammatory and apoptotic markers, while increasing antioxidant activities in mice with cyclophosphamide-induced cardiotoxicity. Moreover, it remarkably alleviated histopathological aberrations in cardiac tissue. Conclusions: Geraniol attenuates cyclophosphamide-induced cardiotoxicity via antioxidant, anti-inflammatory, and antiapoptotic effects.

Abstract Objectives Trauma registries are essential tools for improving trauma care quality and efficiency, but many fail to capture long-term patient-reported outcome measures (PROMs). Focusing on these outcomes is crucial for understanding the extent of disability patients experience and identifying potential post-discharge interventions to optimize recovery. Studies reflecting the experience from low- and middle-income countries in this area are limited. Therefore, we aim to develop a digital trauma registry in Pakistan to prospectively capture patient-reported outcome measures at one, three, six, and twelve months post-injury. Methods We will develop and implement a digital trauma registry at two tertiary care facilities in Karachi, Pakistan: Aga Khan University Hospital and Jinnah Postgraduate Medical Center. The registry will include all admitted adult trauma patients (≥ 18 years). Data collection will be conducted digitally using tablets, with mortality, level of disability, functional status, and quality of life as primary outcomes. Follow-up data will be collected through telephone interviews with patients and caregivers. We will employ descriptive statistics to summarize participant’s socio-demographic and clinical characteristics. Additionally, we will perform survival analysis using Kaplan-Meier curves and Cox proportional hazard models and utilize mixed-effects linear regression to adjust for potential confounders for primary outcomes. Discussion The trauma registry will fill the current gap in knowledge regarding long-term outcomes among trauma patients in low- and middle-income countries (LMICs). This study will delineate future direction for capturing post-discharge data, enhancing our understanding of recovery, and informing the design of interventions aimed at improving long-term outcomes.

Abstract We investigate the propagation characteristics of transverse electric (TE) and transverse magnetic (TM) modes in a semiconductor quantum plasma-filled coaxial waveguide with a Piet Hein cross-section. The unique geometry of the Piet Hein cross-section offers intriguing possibilities for tailoring modal properties and exploiting novel nonlinear phenomena. Using analytical and numerical methods, we unveil the non-monochromatic behaviour of TE and TM modes, characterized by distinct peaks and troughs in their field components. The $$E_{z}$$ E z component of the TM mode exhibits a suppressed field within the central core and higher concentration in the cladding region, potentially minimizing energy loss within the waveguide. The $$E_{\rho }$$ E ρ component exhibits pronounced oscillations near the waveguide boundaries, suggesting constructive and destructive interference patterns. The $$E_{\xi }$$ E ξ component displays a non-monotonic behaviour with multiple pits and bumps, highlighting the interplay between the TE mode, the Piet Hein geometry, and the semiconductor quantum plasma properties. The $$B_{\rho }$$ B ρ component of the TM mode exhibits a non-monochromatic behaviour with multiple maxima and minima, attributed to the complex interactions between propagating waves with different phase shifts. The $$B_{\xi }$$ B ξ component exhibits a non-uniform distribution with distinct pits and bumps, with a resurgence towards the waveguide edges potentially due to higher-order mode contributions and local field enhancement effects. Our findings pave the way for the development of novel photonic devices with enhanced functionalities based on Piet Hein semiconductor quantum plasma waveguides.

Abstract A conformal reflective metasurface fed by a dual-band multiple-input multiple-output (MIMO) antenna is proposed for low-cost beam steering applications in 5G Millimeter-wave frequency bands. The beam steering is accomplished by selecting a specific port of MIMO antenna. Each MIMO port is associated with a beam that points in a different direction due to a conformal reflective metasurface. This novel conformal metasurface antenna design has the advantages of higher gain, lower cost, a simpler feeding source, and a lower profile when compared to traditional reflective metasurfaces using bulky horn antennas and phased arrays with complex feeding networks and phase shifters for beam steering. The proposed beam steering antenna consists of a compact five-element dual-band MIMO and a $$32 \times 32$$ 32 × 32 unit-cell conformal dual-band reflective metasurface placed at the top of the MIMO antenna to obtain the beam steering capability as well as gain enhancement. The proposed reflective metasurface has a stable response under oblique incidence angles of up to $$60^0$$ 60 0 at 24 GHz and 38 GHz and its symmetric, single-layer structure, ensures polarization insensitivity and stable response under conformal conditions. The presented MIMO antenna design is not only compact but also offers a wideband response effectively covering the desired 5G mm-wave frequency bands. The overall size of the MIMO antenna alone is 70 $$\times$$ × 12 $$\hbox {mm}^2$$ mm 2 with a maximum gain of 5.4 and 7.2 dB. It is further improved up to 13.1 and 14.2 dB at 24 and 38 GHz respectively, with a beam steering range of ś $$40^0$$ 40 0 by using a conformal reflective metasurface. Unlike the existing beam steering strategies, the suggested method is not only cost-effective but also increases the overall directivity and gain of the source MIMO antenna. The measured results agree with the simulated results, making it a potential candidate in the 5G and beyond beam steering applications.

Abstract Background Emergency contraceptive pills (ECPs) are significantly underutilized in many parts of the world, especially in South-Asia. Within Pakistan, despite the population surge, the utilization of ECPs remains alarmingly low. The purpose of this study was to investigate the knowledge and practices regarding ECPs amongst married women and men in Karachi, Pakistan and the possible barriers hindering access to and use of these contraceptives. Methodology A multi-centric cross-sectional study was conducted in Karachi, Pakistan. Interviews were conducted with participants using validated questionnaire involving different sections on demographics, knowledge, practices, and barriers to ECP use. Categorical variables were summarized as frequencies and percentages while continuous variables were characterized using mean and standard deviation. A chi-square test of variance was used for association between knowledge scores and different categorical values. A p-value of

Abstract Muscle wasting is a serious complication in heart failure patients. Oxidative stress and inflammation are implicated in the pathogenesis of muscle wasting. Oxidative stress leads to the formation of toxic lipid peroxidation products, such as 4-hydroxy-2-nonenal (HNE), which covalently bind with proteins and DNA and activate atrophic pathways. Whether the formation of lipid peroxidation products and metabolic pathways that remove these toxic products are affected during heart failure-associated skeletal muscle wasting has never been studied. Male C57BL/6J mice were subjected to sham and transverse aortic constriction (TAC) surgeries for 4, 8 or 14 weeks. Different skeletal muscle beds were weighed, and the total cross-sectional area of the gastrocnemius muscle was measured via immunohistochemistry. Muscle function and muscle stiffness were measured by a grip strength meter and atomic force microscope, respectively. Atrophic and inflammatory marker levels were measured via qRT‒PCR. The levels of acrolein and HNE-protein adducts, aldehyde-removing enzymes, the histidyl dipeptide-synthesizing enzyme carnosine synthase (CARNS), and amino acid transporters in the gastrocnemius muscle were measured via Western blotting and qRT‒PCR. Histidyl dipeptides and histidyl dipeptide aldehyde conjugates in the Gastrocnemius and soleus muscles were analyzed by LC/MS–MS. Body weight, gastrocnemius muscle and soleus muscle weights and the total cross-sectional area of the gastrocnemius muscle were decreased after 14 weeks of TAC. Heart weight, cardiac function, grip strength and muscle stiffness were decreased in the TAC-operated mice. Expression of the atrophic and inflammatory markers Atrogin1 and TNF-α, respectively, was increased ~ 1.5–2fold in the gastrocnemius muscle after 14 weeks of TAC (p

Two rehydration protocols currently exist to treat diabetic ketoacidosis (DKA) in pediatric patients aged

Solubility enhancement of poorly aqueous-soluble drugs, like Terbinafine (TBN), is a critical challenge in formulating effective dosage forms. This study focused on developing β-cyclodextrin (β-CD) and polyacrylamide (PAM)-based microgels to address the solubility issue of TBN, classified as a biopharmaceutics classification system class II drug. The microgels were crafted through free radical polymerization, employing methylene bisacylamide as a cross-linker and methacrylic acid as a monomer, initiated by ammonium persulfate. Comprehensive characterizations, including Fourier transform infrared, thermo-gravimetric analysis, differential scanning calorimetry, scanning electron microscopy, powder X-ray diffractometry analysis, Zeta size, and Zeta potential, were conducted. In vitro studies, such as drug release and swelling, were performed at pH 1.2. Toxicity analysis in rabbits revealed zero toxicity. These β-CD/PAM microgels successfully enhanced the solubility of TBN.

Abstract New generation of electrochemical energy storage devices (EESD) such as supercapattery is being intensively studied as it merges the ideal energy density of batteries and optimal power density of supercapacitors in a single device. A multitude of parameters such as the method of electrodes preparation can affect the performance of supercapattery. In this research, nickel doped tin sulfide /tin oxide (SnS@Ni/SnO2) heterostructures were grown directly on the Ni foam and subjected to different calcination temperatures to study their effect on formation, properties, and electrochemical performance through X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and electrochemical tests. The optimized SnS@Ni/SnO2 electrode achieved a maximum specific capacity of 319 C g− 1 while activated carbon based capacitive electrode exhibited maximum specific capacitance of 381.19 Fg− 1. Besides, capacitive electrodes for the supercapattery were optimized by incorporating different conductive materials such as acetylene black (AB), carbon nanotubes (CNT) and graphene (GR). Assembling these optimized electrodes with the aid of charge balancing equation, the assembled supercapattery was able to achieve outstanding maximum energy density and power density of 36.04 Wh kg− 1 and 12.48 kW kg− 1 with capacity retention of 91% over 4,000 charge/discharge cycles.

Abstract Background It is well-known that serum uric acid (SUA) can increase the risk of hypertension, diabetes, obesity and dyslipidemia. However, its independent association with the risk of cardiovascular diseases (CVD) is controversial particularly in different populations. Hence, this study was aimed to assess an independent association of SUA with CVD risk in a Punjabi Pakistani cohort. Methods This is a retrospective observational study in which 502 human subjects having CVD, hypertension and/or diabetes were grouped based on SUA levels as normouricemia (n = 266) and hyperuricemia (n = 236). Role of SUA was assessed in increasing the risk of CVD independent of other key confounding factors (i.e. age, gender, dyslipidemia, hypertension, diabetes, dietary and life-style habits). All clinical and biochemical data were analyzed in SPSS (ver. 20). Results Subjects aged 55 ± 13 years were of both genders (males: 52%). SUA levels were significantly different among clinical subtypes of CVD [i.e. acute coronary syndrome (ACS), myocardial infarction (MI) and heart failure (HF)]. Spearman correlation showed a significantly positive association between CVD and SUA (rho = 0.149, p

Abstract Dinanath grass (Pennisetum pedicellatum Trin.) is an extensively grown forage grass known for its significant drought resilience. In order to comprehensively grasp the adaptive mechanism of Dinanath grass in response to water deficient conditions, transcriptomic and metabolomics were applied in the leaves of Dinanath grass exposed to two distinct drought intensities (48-hour and 96-hour). Transcriptomic analysis of Dinanath grass leaves revealed that a total of 218 and 704 genes were differentially expressed under 48- and 96-hour drought conditions, respectively. The genes that were expressed differently (DEGs) and the metabolites that accumulated in response to 48-hour drought stress mainly showed enrichment in the biosynthesis of secondary metabolites, particularly phenolics and flavonoids. Conversely, under 96-hour drought conditions, the enriched pathways predominantly involved lipid metabolism, specifically sterol lipids. In particular, phenylpropanoid pathway and brassinosteroid signaling played a crucial role in drought response to 48- and 96-hour water deficit conditions, respectively. This variation in drought response indicates that the adaptation mechanism in Dinanath grass is highly dependent on the intensity of drought stress. In addition, different genes associated with phenylpropanoid and fatty acid biosynthesis, as well as signal transduction pathways namely phenylalanine ammonia-lyase, putrescine hydroxycinnamoyl transferase, abscisic acid 8’-hydroxylase 2, syntaxin-61, lipoxygenase 5, calcium-dependent protein kinase and phospholipase D alpha one, positively regulated with drought tolerance. Combined transcriptomic and metabolomic analyses highlights the outstanding involvement of regulatory pathways related to secondary cell wall thickening and lignin biosynthesis in imparting drought tolerance to Dinanath grass leaves. These findings collectively contribute to an enhanced understanding of candidate genes and key metabolites relevant to drought response in Dinanath grass. Furthermore, they establish a groundwork for the creation of a transcriptome database aimed at developing abiotic stress-tolerant grasses and major crop varieties through both transgenic and genome editing approaches.

Abstract The popular organophosphorus (OP) compound chlorpyrifos (CP) has recently gained significant attention due to its health risks, particularly among farmers exposed to OP pesticides. This study aimed to evaluate the acute toxicity of Cuscuta reflexa seed extract (CRSE) and its efficacy of mitigating the adverse effects of CP in albino male mice. For acute toxicity analysis, the first group was served as the control group, while the second group was received CRSE (200 mg/kg/bw) on the first day of the 14-day experiment. For hepatotoxicity analysis, the first group was the control group, the second group (vehicle control) received corn oil (CO) (2 mL/kg/bw), the third group was given CP (20 mg/kg/bw) dissolved in corn oil and the fourth group was given CP (20 mg/kg/bw) along-with CRSE (200 mg/kg/bw) orally via gavage once daily for 21 days. The acute toxicity examination revealed no statistically significant differences between the CRSE-treated and control groups in serum biochemical indicators and histopathological analyses of various organs, suggesting that CRSE as safe at a dosage of 200 mg/kg/bw, with an oral LD50 in mice higher than 200 mg/kg. The hepatotoxicity study demonstrated that the CP administration resulted in liver damage and oxidative stress, while CRSE acted as an antioxidant and attenuated the signs of oxidative stress in liver damage. Hence, a promising therapeutic approach for lowering CP hepatotoxicity is co-treatment with CRSE.

Neural machine translation (NMT) approaches driven by artificial intelligence (AI) has gained more and more attention in recent years, mainly due to their simplicity yet state-of-the-art performance. Despite NMT models with attention mechanism relying heavily on the accessibility of substantial parallel corpora, they have demonstrated efficacy even for languages with limited linguistic resources. The convolutional neural network (CNN) is frequently employed in tasks involving visual and speech recognition. Implementing CNN for MT is still challenging compared to the predominant approaches. Recent research has shown that the CNN-based NMT model cannot capture long-term dependencies present in the source sentence. The CNN-based model can only capture the word dependencies within the width of its filters. This unnatural character often causes a worse performance for CNN-based NMT than the RNN-based NMT models. This study introduces a simple method to improve neural translation of a low-resource language, specifically Urdu-English (UR-EN). In this paper, we use a Fully Convolutional Neural Network (FConv-NN) based NMT architecture to create a powerful MT encoder for UR-EN translation that can capture the long dependency of words in a sentence. Although the model is quite simple, it yields strong empirical results. Experimental results show that the FConv-NN model consistently outperforms the traditional CNN-based model with filters. On the Urdu-English Dataset, the FConv-NN model produces translation with a gain of 18.42 BLEU points. Moreover, the quantitative and comparative analysis shows that in a low-resource setting, FConv-NN-based NMT outperforms conventional CNN-based NMT models.

The heat transfer analysis and time dependent magneto hydrodynamics boundary layer flow of three dimensional viscous incompressible Newtonian fluid in rotating frame across the extending surface subject to microgravity g∗(t)(=go[1+ϵcos(πωt)]k) is investigated. The gravity modulation and gradient of fluid temperature typically generate buoyant convective flows in case of various situation, most likely in environment of low gravity or microgravity, the systems can operate under purely diffusive conditions in this environment since buoyancy-driven fluid flows and sedimentations are significantly diminished. Recent years have seen a growing interest of researchers in mixed convection that is regulated by microgravity environment due to highly extensive modern technological applications. The innovative aspect of the current study is the consideration of the impacts of gravity jitters (g-jitter) in rotating Newtonian fluid. The leading governing equations of elaborated fluid flow problem transformed into form of non dimensional coupled non-linear PDEs (partial differential equations) by the help of appropriate similarity functions. For seek of numerical solution, a very powerful technique, finite element method is used via MATLAB. The flexibility, robustness, and exactness of this technique make it a strong tool for solving fluid flow problems. The magnitude of average Nusselt number exhibits 16% & 17% decline against 46.31% increment in Coriolis and magnetic forces. Rising the amplitude of modulation results in proportional rise and decline in skin friction and heat transfer. By increasing strength of rotating parameter, magnetic parameter and frequency modulation parameter, the velocity profile declined while, temperature field has opposite trend against these parameters. The comparison of current results with existing results is given and found a very good agreement between them.

Abstract This work initiates a concept of reduced reverse degree based $$\mathcal {RR_D\,M}$$ RR D M -Polynomial for a graph, and differential and integral operators by using this $$\mathcal {RR_D\,M}$$ RR D M -Polynomial. In this study twelve reduced reverse degree-based topological descriptors are formulated using the $$\mathcal {RR_D\,M}$$ RR D M -Polynomial. The topological descriptors, denoted as $$\mathbb{T}\mathbb{D}$$ T D ’s, are numerical invariants that offer significant insights into the molecular topology of a molecular graph. These descriptors are essential for conducting QSPR investigations and accurately estimating physicochemical attributes. The structural and algebraic characteristics of the graphene and graphdiyne are studied to apply this methodology. The study involves the analysis and estimation of Reduced reverse degree-based topological descriptors and physicochemical features of graphene derivatives using best-fit quadratic regression models. This work opens up new directions for scientists and researchers to pursue, taking them into new fields of study.

Abstract Task scheduling problem (TSP) is huge challenge in cloud computing paradigm as number of tasks comes to cloud application platform vary from time to time and all the tasks consists of variable length, runtime capacities. All these tasks may generated from various heterogeneous resources which comes onto cloud console directly effects the performance of cloud paradigm with increase in makespan, energy consumption, resource costs. Traditional task scheduling algorithms cannot handle these type of complex workloads in cloud paradigm. Many authors developed Task Scheduling algorithms by using metaheuristic techniques, hybrid approaches but all these algorithms give near optimal solutions but still TSP is a highly challenging and dynamic scenario as it resembles NP hard problem. Therefore, to tackle the TSP in cloud computing paradigm and schedule the tasks in an effective way in cloud paradigm, we formulated Adaptive Task scheduler which segments all the tasks comes to cloud console as sub tasks and fed these to the scheduler which is modeled by Improved Asynchronous Advantage Actor Critic Algorithm(IA3C) to generate schedules. This scheduling process is carried out in two stages. In first stage, all incoming tasks are segmented as sub tasks. After segmentation, all these sub tasks according to their size, execution time, communication time are grouped together and fed to the (ATSIA3C) scheduler. In the second stage, it checks for the above said constraints and disperse them onto the corresponding suitable processing capacity VMs resided in datacenters. Proposed ATSIA3C is simulated on Cloudsim. Extensive simulations are conducted using both fabricated worklogs and as well as realtime supercomputing worklogs. Our proposed mechanism evaluated over baseline algorithms i.e. RATS-HM, AINN-BPSO, MOABCQ. From results it is evident that our proposed ATSIA3C outperforms existing task schedulers by improving makespan by 70.49%. Resource cost is improved by 77.42%. Energy Consumption is improved over compared algorithms 74.24% in multi cloud environment by proposed ATSIA3C.

In this research, we leverage various $ q $-calculus identities to introduce the notion of $ q $-Hermite-Appell polynomials involving three variables, elucidating their formalism. We delve into numerous properties and unveil novel findings regarding these $ q $-Hermite-Appell polynomials, encompassing their generating function, series representation, summation equations, recurrence relations, $ q $-differential formula, and operational principles. Our investigation sheds light on the intricate nature of these polynomials, elucidating their behavior and facilitating deeper understanding within the realm of $ q $-calculus.

Abstract Ultra-wideband (UWB) technology is extensively used in indoor navigation, medical applications, and Internet of Things devices due to its low power consumption and resilience against multipath fading and losses. This paper examines a multiple-input multiple-output (MIMO), circularly polarized (CP) dielectric resonator antenna for UWB systems. Compact form factor, high gain, wideband response, improved port isolation, and high data rates are the major design goals. This arrangement consists of two identical DRAs with self-decoupled orthogonal orientations eliminating the need for extra decoupling structures while achieving an impressive maximum isolation of 43 dB. The corner-edge feeding mechanism of the extended feedline generates two orthogonal E-fields, facilitating circular polarization. Additionally, a printed hook-shaped stub integrated with the ground plane enhances CP performance across the two operating bands without altering the DR structure. Fabrication and testing exhibit an impressive 133 $$\%$$ % impedance bandwidth (2.5–14 GHz) with high port isolation. For a 3 dB axial ratio reference, the single-element design exhibits axial ratio bandwidths (ARBW) of 1.2 GHz (3.6–4.8 GHz) and 0.8 GHz (9.3–10.1 GHz). Remarkably, the MIMO configuration achieves a single ARBW of 0.5 GHz (3.9–4.4 GHz). Detailed investigations of MIMO performance parameters, including diversity gain, envelope correlation coefficient, channel capacity loss, and total active reflection coefficient, underscore the design’s efficacy, making it a good choice for UWB wireless applications.

Abstract Circulating tumour cells (CTCs), derived from primary tumours, play a pivotal role in cancer metastasis by migrating into the peripheral bloodstream. These cells are paramount in clinical research, serving as early diagnostic markers for metastatic cancer. Analysing CTC counts and their biomarker characteristics can provide invaluable insights into tumour identification, profiling, and metastatic capabilities. However, the rarity and diverse nature of CTCs in the bloodstream present significant challenges to their isolation and detection, especially in the initial stages of metastasis. Recent advancements in nanotechnology have led to the development of innovative CTC separation and detection methods. This review focuses on applying nanoparticles, nanomaterials, and microfluidic platforms to simplify the isolation and detection of CTCs. The infusion of nanotechnology in this field marks a crucial turning point, enabling the necessary progress to advance CTC research.

Abstract In order to explore the role of topological indices for predicting physio-chemical properties of anti-HIV drugs, this research uses python program-based algorithms to compute topological indices as well as machine learning algorithms. Degree-based topological indices are calculated using Python algorithm, providing important information about the structural behavior of drugs that are essential to their anti-HIV effectiveness. Furthermore, machine learning algorithms analyze the physio-chemical properties that correspond to anti-HIV activities, making use of their ability to identify complex trends in large, convoluted datasets. In addition to improving our comprehension of the links between molecular structure and effectiveness, the collaboration between machine learning and QSPR research further highlights the potential of computational approaches in drug discovery. This work reveals the mechanisms underlying anti-HIV effectiveness, which paves the way for the development of more potent anti-HIV drugs. This work reveals the mechanisms underlying anti-HIV efficiency, which paves the way for the development of more potent anti-HIV drugs which demonstrates the invaluable advantages of machine learning in assessing drug properties by clarifying the biological processes underlying anti-HIV behavior, which paves the way for the design and development of more effective anti-HIV drugs.

Abstract Independent control of electromagnetic (EM) waves by metasurfaces for multiple tasks are highly desired and is the recent hot topic of research. In this work we contribute a polarization insensitive frequency multiplexed 2-bit coding metasurface to control the Terahertz (THz) waves in the two operating bands independently. In this regard, as a first step a cascaded meta-atom composed of square rings and/or square metallic patches separated by two polyimide substrates is designed and optimized that provides sixteen independent distinct discrete phases in the reflection geometry. These meta-atoms are then distributed with distinct coding sequences in the two-dimensional spatial plane to realize various bi-functional metasurfaces. As a proof of the concept various full structures are designed and simulated to realize a series of bi-functionalities including anomalous reflection/beam shaping, beam shaping/anomalous reflection, beam deflection/Orbital angular momentum (OAM) beam generation with distinct modes and propagating wave to surface wave (PW–SW) conversion/PW beam manipulation in the lower and higher THz bands, respectively. All the simulation results are in excellent agreement with their theoretical equivalents. We envision that the proposed meta-designs have potential applications for the multi-spectral control of EM waves in THz band. The idea can be further extended to design frequency dependent tri-functional and multi-functional THz meta-devices.

Abstract Artificial Intelligence (AI) is being applied to improve the efficiency of software systems used in various domains, especially in the health and forensic sciences. Explainable AI (XAI) is one of the fields of AI that interprets and explains the methods used in AI. One of the techniques used in XAI to provide such interpretations is by computing the relevance of the input features to the output of an AI model. File fragment classification is one of the vital issues of file carving in Cyber Forensics (CF) and becomes challenging when the filesystem metadata is missing. Other major challenges it faces are: proliferation of file formats, file embeddings, automation, We leverage and utilize interpretations provided by XAI to optimize the classification of file fragments and propose a novel sifting approach, named SIFT (Sifting File Types). SIFT employs TF-IDF to assign weight to a byte (feature), which is used to select features from a file fragment. Threshold-based LIME and SHAP (the two XAI techniques) feature relevance values are computed for the selected features to optimize file fragment classification. To improve multinomial classification, a Multilayer Perceptron model is developed and optimized with five hidden layers, each layer with $$i \times n$$ i × n neurons, where i = the layer number and n = the total number of classes in the dataset. When tested with 47,482 samples of 20 file types (classes), SIFT achieves a detection rate of 82.1% and outperforms the other state-of-the-art techniques by at least 10%. To the best of our knowledge, this is the first effort of applying XAI in CF for optimizing file fragment classification.

This study aims to analyze how two different types of investments (local domestic and foreign direct investments at the provincial level) and revenues (booked by the provincial governments, general allocation funds, special allocation funds, local taxes, and retribution) affect investment loan decisions by local government banks in Indonesia. The study uses panel data applying ordinary least squares and multiple linear regression. Thus, 144 data were sampled from 2013 to 2021 from 16 local government banks out of Java Island in 21 provinces in Indonesia. The study found that local domestic investment at the provincial level affects investment loan decisions by local government banks. In contrast, foreign direct investment did not affect lending decisions, indicating that local domestic investment contributes to the real local economy at the provincial level. Different results were found in provincial revenues in the form of general and special allocation funds, which negatively affected loan investment decisions, possibly due to provincial revenues utilized to cover the financial deficit and capital expenditure spent chiefly on imported goods. Additionally, local taxes at the provincial level also negatively affect investment loan decisions, possibly due to fluctuations in local tax collection during COVID-19. However, the study found that local retribution contributes to predicting loan investment decisions, suggesting revenue collection by the governments considering local economic conditions. The study findings suggest that provincial governments should direct investments that can impact the local economy and spend their revenues on goods and services that can drive local economic growth. AcknowledgmentThe investment loan made by local government banks, which is associated with local investment and revenue, is part of a research grant project from the Economics and Business Faculty of Hasanuddin University. This project is a result of collaboration with national and international researchers. In carrying out this research, some inputs from people working in local government banks, financial service authorities, and provincial and regency governments are addressed.Thanks to Ahmadi Usman for secondary data and Syahidah Ulhaq for some application programs enabling mapping literature, as well as Israa Natiq Jabar for supervising the result and applied some inputs in the section of research method.

Abstract RNA modifications play an important role in actively controlling recently created formation in cellular regulation mechanisms, which link them to gene expression and protein. The RNA modifications have numerous alterations, presenting broad glimpses of RNA’s operations and character. The modification process by the TET enzyme oxidation is the crucial change associated with cytosine hydroxymethylation. The effect of CR is an alteration in specific biochemical ways of the organism, such as gene expression and epigenetic alterations. Traditional laboratory systems that identify 5-hydroxymethylcytosine (5hmC) samples are expensive and time-consuming compared to other methods. To address this challenge, the paper proposed XGB5hmC, a machine learning algorithm based on a robust gradient boosting algorithm (XGBoost), with different residue based formulation methods to identify 5hmC samples. Their results were amalgamated, and six different frequency residue based encoding features were fused to form a hybrid vector in order to enhance model discrimination capabilities. In addition, the proposed model incorporates SHAP (Shapley Additive Explanations) based feature selection to demonstrate model interpretability by highlighting the high contributory features. Among the applied machine learning algorithms, the XGBoost ensemble model using the tenfold cross-validation test achieved improved results than existing state-of-the-art models. Our model reported an accuracy of 89.97%, sensitivity of 87.78%, specificity of 94.45%, F1-score of 0.8934%, and MCC of 0.8764%. This study highlights the potential to provide valuable insights for enhancing medical assessment and treatment protocols, representing a significant advancement in RNA modification analysis.

Abstract A green building (GB) is a design idea that integrates environmentally conscious technology and sustainable procedures throughout the building’s life cycle. However, because different green requirements and performances are integrated into the building design, the GB design procedure typically takes longer than conventional structures. Machine learning (ML) and other advanced artificial intelligence (AI), such as DL techniques, are frequently utilized to assist designers in completing their work more quickly and precisely. Therefore, this study aims to develop a GB design predictive model utilizing ML and DL techniques to optimize resource consumption, improve occupant comfort, and lessen the environmental effect of the built environment of the GB design process. A dataset ASHARE-884 is applied to the suggested models. An Exploratory Data Analysis (EDA) is applied, which involves cleaning, sorting, and converting the category data into numerical values utilizing label encoding. In data preprocessing, the Z-Score normalization technique is applied to normalize the data. After data analysis and preprocessing, preprocessed data is used as input for Machine learning (ML) such as RF, DT, and Extreme GB, and Stacking and Deep Learning (DL) such as GNN, LSTM, and RNN techniques for green building design to enhance environmental sustainability by addressing different criteria of the GB design process. The performance of the proposed models is assessed using different evaluation metrics such as accuracy, precision, recall and F1-score. The experiment results indicate that the proposed GNN and LSTM models function more accurately and efficiently than conventional DL techniques for environmental sustainability in green buildings.

Abstract Concrete is used worldwide as a construction material in many projects. It exhibits a brittle nature, and fibers' addition to it improves its mechanical properties. Polypropylene (PP) fibers stand out as widely employed fibers in concrete. However, conventional micro-PP fibers pose challenges due to their smooth texture, affecting bonding within concrete and their propensity to clump during mixing due to their thin and soft nature. Addressing these concerns, a novel type of PP fiber is proposed by gluing thin fibers jointly and incorporating surface indentations to enhance mechanical anchorage. This study investigates the incorporation of macro-PP fibers into high-strength concrete, examining its fresh and mechanical properties. Three different concrete strengths 40 MPa, 45 MPa, and 50 MPa, were studied with fiber content of 0–1.5% v/f. ASTM specifications were utilized to test the fresh and mechanical properties, while the RILEM specifications were adopted to test the bond of bar reinforcements in concrete. Test results indicate a decrease in workability, increased air content, and no substantial shift in fresh concrete density. Hardened concrete tests, adding macro-PP fibers, show a significant increase in splitting tensile strength, bond strength, and flexural strength with a maximum increase of 34.5%, 35%, and 100%, respectively. Concrete exhibits strain-hardening behavior with 1% and 1.5% fiber content, and the flexural toughness increases remarkably from 2.2 to 47.1. Thus, macro PP fibers can effectively improve concrete's mechanical properties and resistance against crack initiation and spread.

Samiah Shahid,1,2 Ayesha Khan,1 Wajeehah Shahid,3 Mehvesh Rehan,2 Roha Asif,1 Haseeb Nisar,4 Qudsia Kanwal,5 Jeong Ryeol Choi6 1Research Centre for Health Sciences (RCHS), The University of Lahore, Lahore, Pakistan; 2Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore, Lahore, Pakistan; 3Department of Physics, The University of Lahore, Lahore, Pakistan; 4School of Life Sciences, University of Management and Technology, Lahore, Pakistan; 5Department of Chemistry, The University of Lahore, Lahore, Pakistan; 6School of Electronic Engineering, Kyonggi University, Suwon, Kyeonggi-do, 16227, Republic of KoreaCorrespondence: Samiah Shahid, Research Centre for Health Sciences (RCHS), Associate Professor, Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore, Lahore, 54590, Pakistan, Email samiah.shahid@yahoo.com Jeong Ryeol Choi, School of Electronic Engineering, Kyonggi University, Suwon, Kyeonggi-do, 16227, Republic of Korea, Email choiardor@hanmail.netAbstract: Breast and ovarian cancers, despite having chemotherapy and surgical treatment, still have the lowest survival rate. Experimental stages using nanoenzymes/nanozymes for ovarian cancer diagnosis and treatment are being carried out, and correspondingly the current treatment approaches to treat breast cancer have a lot of adverse side effects, which is the reason why researchers and scientists are looking for new strategies with less side effects. Nanoenzymes have intrinsic enzyme-like activities and can reduce the shortcomings of naturally occurring enzymes due to the ease of storage, high stability, less expensive, and enhanced efficiency. In this review, we have discussed various ways in which nanoenzymes are being used to diagnose and treat breast and ovarian cancer. For breast cancer, nanoenzymes and their multi-enzymatic properties can control the level of reactive oxygen species (ROS) in cells or tissues, for example, oxidase (OXD) and peroxidase (POD) activity can be used to generate ROS, while catalase (CAT) or superoxide dismutase (SOD) activity can scavenge ROS. In the case of ovarian cancer, most commonly nanoceria is being investigated, and also when folic acid is combined with nanoceria there are additional advantages like inhibition of beta galactosidase. Nanocarriers are also used to deliver small interfering RNA that are effective in cancer treatment. Studies have shown that iron oxide nanoparticles are actively being used for drug delivery, similarly ferritin carriers are used for the delivery of nanozymes. Hypoxia is a major factor in ovarian cancer, therefore MnO2-based nanozymes are being used as a therapy. For cancer diagnosis and screening, nanozymes are being used in sonodynamic cancer therapy for cancer diagnosis and screening, whereas biomedical imaging and folic acid gold particles are also being used for image guided treatments. Nanozyme biosensors have been developed to detect ovarian cancer. This review article summarizes a detailed insight into breast and ovarian cancers in light of nanozymes-based diagnostic and therapeutic approaches. Keywords: breast cancer, ovarian cancer, nanoenzymes, diagnosis, treatment

Abstract Diabetes mellitus (DM) is a rapidly prevailing disease throughout the world that poses boundless risk factors linked to several health problems. Vildagliptin is the standard dipeptidyl peptidase-4 (DPP-4) inhibitor type of medication that is used for the treatment of diabetes anti-hyperglycemic agent (anti-diabetic drug). The current study aimed to synthesize vildagliptin-loaded ZnO NPs for enhanced efficacy in terms of increased retention time minimizing side effects and increased hypoglycemic effects. Herein, Zinc Oxide (ZnO) nanoparticles (NPs) were constructed by precipitation method then the drug vildagliptin was loaded and drug loading efficiency was estimated by the HPLC method. X-ray diffraction analysis (XRD), UV–vis spectroscopy, FT-IR, scanning electron microscope (SEM), and EDX analysis were performed for the characterization of synthesized vildagliptin-loaded ZnO NPs. The UV–visible spectrum shows a distinct peak at 363 nm which confirms the creation of ZnO NPs and SEM showed mono-dispersed sphere-shaped NPs. EDX analysis shows the presence of desired elements along with the elemental composition. The physio-sorption studies, which used adsorption isotherms to assess adsorption capabilities, found that the Freundlich isotherm model explains the data very well and fits best. The maximum adsorption efficiency of 58.83% was obtained. Further, In vitro, anti-diabetic activity was evaluated by determining the α-amylase and DPP IV inhibition activity of the product formed. The formulation gave maximum inhibition of 82.06% and 94.73% of α-amylase and DPP IV respectively. While at 1000 µg/ml concentration with IC50 values of 24.11 μg/per ml and 42.94 μg/ml. The inhibition of α-amylase can be ascribed to the interactive effect of ZnO NPs and vildagliptin.

This study introduces a new approach to the development of generalized 1-parameter, 2-variable Hermite–Frobenius–Euler polynomials, which are characterized by their generating functions, series definitions and summation formulae. Additionally, the research utilizes a factorization method to establish recurrence relations, shift operators and various differential equations, including differential, integro-differential and partial differential equations. The framework elucidates the fundamental properties of these polynomials by utilizing generating functions, series definitions and summation formulae. The results of the study contribute to the understanding of the properties of these polynomials and their potential applications.

AbstractIn the development of hybrid special polynomials, it is essential to incorporate the monomiality principle, operational rules, and other related properties. This research study adopts this approach and introduces a novel polynomial type called the two-iterated degenerate Appell polynomials, utilizing the monomiality principle. This study yields new findings that align with previous research efforts. The study presents explicit formulas and key properties of these polynomials, while also establishing connections with other polynomial types such as the Bernoulli, Euler, and Genocchi polynomials. These connections facilitate the derivation of additional results. By employing the monomiality principle and considering the aforementioned polynomials as initial members of the Appell family, this study contributes to the expansion of knowledge in the field of special polynomials.

Background: Traumatic brain injury (TBI) is an increasing widespread cause of disability and mortality, typically leading to dementia and memory impairment. Objective: This study aims to investigate the neuroprotective potential of Nigella sativa extract against TBI induced memory impairment in adult albino mice. Methods: Adult male mice were divided into four groups randomly: Control, Nigella sativa extract alone, TBI alone and TBI plus Nigella sativa extract. TBI induction was carried out in mice using a weight dropping method then Nigella sativa extract (10 mg/kg) was administered intraperitoneally for two weeks. Morris water maze and Y-maze tests were used to measure memory improvement ability and Western blot technique was used to analyse the neuroinflammatory and synaptic protein markers. Results: Nigella sativa extract significantly decreased phosphorylated c-Jun N-terminal kinase (p-JNK), Tumor necrosis factor-alpha (TNF-α), and nuclear factor kappa B (NF-κB) proteins to reduce TBI-induced neuroinflammation accompanied by the restoration of both pre- and post-synaptic protein expression in adult mice model. Furthermore, Nigella sativa extract enhanced both short and long-term spatial memory against TBI in adult mice model. Conclusion: Nigella sativa extract abrogated neuroinflammation mediated memory impairment in TBI mice model. Further research is needed to determine Nigella sativa extract ingredients detail completely and to understand its mechanisms of neuroprotection in reducing memory impairments associated with traumatic brain injury and other neurodegenerative diseases.

We present a novel framework for introducing generalized 3-variable 1-parameter Hermite-based Appell polynomials. These polynomials are characterized by generating function, series definition, and determinant definition, elucidating their fundamental properties. Moreover, utilizing a factorization method, we established recurrence relations, shift operators, and various differential equations, including differential, integrodifferential, and partial differential equations. Special attention is given to exploring the specific cases of 3-variable 1-parameter generalized Hermite-based Bernoulli, Euler, and Genocchi polynomials, offering insights into their unique features and applications.

Abstract Green building (GB) techniques are essential for reducing energy waste in the construction sector, which accounts for almost 40% of global energy consumption. Despite their importance, challenges such as occupant behavior and energy management gaps often result in GBs consuming up to 2.5 times more energy than intended. To address this, Building Automation Systems (BAS) play a crucial role in enhancing energy efficiency. This research develops a predictive model for GB design using machine learning to minimize energy consumption and improve indoor sustainability. The dataset is utilized to predict cooling and heating individually, with data visualization by graphically illustrating dataset features and preprocessing through Z-Score normalization and dataset splitting. The proposed model, based on active learning and utilizing ML regressors such as Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), CatBoost (CB), Light Gradient Boosting Machine (LGBM), K-Nearest Neighbor (KNN), and Logistic Regressor (LR), shows significant performance improvements. The CBR-AL model achieves impressive results with values of 0.9975 for cooling (Y1) and 0.9883 for heating (Y2), indicating a high level of accuracy. The model’s success in reducing energy consumption and improving sustainability has potential ripple effects, including substantial cost savings, reduced carbon footprints, and improved operational efficiency in green buildings. This approach not only enhances environmental sustainability but also sets a benchmark for future advancements in predictive modelling for energy management.

Abstract Metal-organic frameworks (MOFs) play a pivotal role in modern material science, offering unique properties such as flexibility, substantial pore space, distinctive structure, and large surface area. Recently, zinc-based MOFs have attracted significant attention, particularly in the biomedical arena, owing to their versatile applications in drug delivery, biosensing, and cancer imaging. However, there remains a crucial need to explore and understand the structural properties of zinc silicate-based MOFs to fully exploit their potential in various applications. The objective of this study is to address this need by employing topological modeling techniques to characterize zinc silicate networks. Utilizing connection number concept of chemical graph theory and novel AL molecular descriptors, we aim to investigate the structural intricacies of these MOFs. More precisely, zinc silicate-based MOF networks are topologically modeled via novel AL topological indices, and derived mathematical closed form formulae for them. By comparing experimental and calculated values and constructing linear regression models, the predictive capabilities of the proposed descriptors are evaluated. Specifically, the performance of derived topological indices against the physico-chemical properties of octane isomers is assessed, which provide valuable insights into their predictive potential. The findings of this study demonstrated the potential of novel AL indices in predicting a wide range of important physico-chemical properties, further enhancing their practicality in materials science and beyond.

Abstract tDCS modulates the activity of the neuronal networks to induce the desired behavioural changes. Two factors determine its effectiveness- (1) whether the network being stimulated is relevant to the task, and (2) if there is a scope for improvement in behavioral performance. To explore this, both dorsal (sub-lexical) and ventral (lexical) reading networks were stimulated (20 min, 2 mA) in 25 healthy young volunteers. Participants performed two reading tasks with different levels of lexical involvement: word fragment completion tasks (WCT) and word association tasks (WAT), while event-related potentials (ERPs) were recorded simultaneously. The study used a within-subject design over three sessions, comparing various electrode montages targeting the dorsal pathway's left inferior parietal lobule or the ventral reading pathway's left middle temporal lobule, as well as sham stimulation. The impact of tDCS sessions (dorsal, ventral, & sham) and task type (WCT & WAT) on priming effects (primed vs. unprimed) of behavioral performance (accuracy and reaction times), and ERP parameters (N400 amplitudes and latencies) were statistically analyzed.It was found that tDCS modulated the performance of WAT only (a task with a lower priming effect). The failure to modulate WCT (larger priming effect) indicated that tDCS was effective for conditions with room for improvement compared to a task where performance has reached the ceiling. Ventral stimulation enhanced accuracy in the WAT condition and shortened the N400 latency of the priming effect. In contrast, dorsal stimulation delayed the priming effect reaction time in the WAT condition and enhanced the N400 amplitude. To conclude, enhancement in performance due to tDCS occurs when the network (ventral) being stimulated aligns with the cognitive demands of the task and there is a scope for improvement.

Abstract Background Anxiety and mood disorders are the main cause of illness in people under the age of 25, accounting for 45% of the global disease burden, whereas 4.6% of teenagers aged 15 to 19 are predicted to experience anxiety. Pakistan country, with a population of 200 million, has the worst mental health indicators and fewer than 500 psychiatrists. Despite the existence of various treatments for anxiety, this goes unrecognized and untreated. Due to a lack of awareness, evaluation, prevention, and interventional programs related to being implemented among adolescents in Pakistan, there is a rise in mental health issues in the earlier years of life. It calls for a critical need for indigenous, evidence-based interventions. The present study aimed to evaluate the feasibility of cognitive behavioral therapy (CBT)-based interventions to reduce anxiety symptoms among school children in Pakistan. Methods This study was a pre-post design, two-arm, single-blinded, feasibility, randomized controlled trial. Thirty-four participants (experimental group, n = 17; control group, n = 17) were recruited from four semi-government schools in Rawalpindi with a mean age of 15 (M = 15, SD = 0.73). Two instruments Beck Anxiety Inventory for Youth (BAI-Y II) and BASC-3 Behavioural and Emotional Screening System Student (BESS-SF) were used to assess the severity of symptoms. Participants in the intervention arm received eight-group therapy CBT sessions. A two-way factorial analysis was used to examine the efficacy of CBT in reducing symptom severity. Results This study’s findings showed that in comparison to the wait-list control group, CBT successfully improved anxiety symptoms among school children while enhancing their social skills. Conclusion This study will help improve the treatment for anxiety in Pakistan by prioritizing school-based intervention and group-based CBT intervention. Trial registration The trial has been registered at the American Economic Association’s registry for randomized controlled trials. RCT ID: AEARCTR-0009551 . Registered 2022–07-04.

Abstract Issue Biomedical approaches want to change locals’ behaviors without understanding the sociocultural rationales and contextualizing the cultural and structural backdrop of women’s agency. Objectives This study explored the perceptions and practices of rural mothers about fertility and reproductive health and further examine the lack of preference for contraception and birth spacing in Southern Pakistan. Methodology Using purposive sampling we recruited 15 healthcare providers and 20 mothers from Southern Punjab. Key informants and in-depth interviews were used for data collection. We extracted themes and sub themes to analyse qualitative data. Findings Five major themes identified preventing birth spacing and contraceptive use: (1) cultural barriers (2) economic difficulties and demographic factors; (3) gender-related hurdles; (4) spiritual and religious obstacles, and (5) medico-ethical complications. Nearly, ten sub-themes contributing to these major themes were: custom of girls’ early marriages, in-laws’ permission for contraception, women’s concern for medical complications and preference for safer methods, misuse of contraceptive methods by the medical community, mothers’ perception of contraception as sinful act and controlling birth is against faith, economic and rural-ethnic factors for high fertility, masculine disapproval of condom use, and wishing to give birth to male children. Suggestions We advocate for understanding the sociocultural explanations for low contraceptive use and urge practice of more natural methods of birth spacing over commercial solutions. The study suggests socio-economic development of less developed communities and empowerment of poor, illiterate, and rural women along with behavior change communication strategies.

Abstract Background Antioxidant proteins are involved in several biological processes and can protect DNA and cells from the damage of free radicals. These proteins regulate the body's oxidative stress and perform a significant role in many antioxidant-based drugs. The current invitro-based medications are costly, time-consuming, and unable to efficiently screen and identify the targeted motif of antioxidant proteins. Methods In this model, we proposed an accurate prediction method to discriminate antioxidant proteins namely StackedEnC-AOP. The training sequences are formulation encoded via incorporating a discrete wavelet transform (DWT) into the evolutionary matrix to decompose the PSSM-based images via two levels of DWT to form a Pseudo position-specific scoring matrix (PsePSSM-DWT) based embedded vector. Additionally, the Evolutionary difference formula and composite physiochemical properties methods are also employed to collect the structural and sequential descriptors. Then the combined vector of sequential features, evolutionary descriptors, and physiochemical properties is produced to cover the flaws of individual encoding schemes. To reduce the computational cost of the combined features vector, the optimal features are chosen using Minimum redundancy and maximum relevance (mRMR). The optimal feature vector is trained using a stacking-based ensemble meta-model. Results Our developed StackedEnC-AOP method reported a prediction accuracy of 98.40% and an AUC of 0.99 via training sequences. To evaluate model validation, the StackedEnC-AOP training model using an independent set achieved an accuracy of 96.92% and an AUC of 0.98. Conclusion Our proposed StackedEnC-AOP strategy performed significantly better than current computational models with a ~ 5% and ~ 3% improved accuracy via training and independent sets, respectively. The efficacy and consistency of our proposed StackedEnC-AOP make it a valuable tool for data scientists and can execute a key role in research academia and drug design.

An early identification and subsequent management of cerebral small vessel disease (cSVD) grade 1 can delay progression into grades II and III. Machine learning algorithms have shown considerable promise in medical image interpretation automation. An experimental cross-sectional study aimed to develop an automated computer-aided diagnostic system based on AI (artificial intelligence) tools to detect grade 1-cSVD with improved accuracy. Patients with Fazekas grade 1 cSVD on Non-Contrast Magnetic Resonance Imaging (MRI) Brain of age >40 years of both genders were included. The dataset was pre-processed to be fed into a 3D convolutional neural network (CNN) model. A 3D stack with the shape (120, 128, 128, 1) containing axial slices from the brain magnetic resonance image was created. The model was created from scratch and contained four convolutional and three fully connected (FC) layers. The dataset was preprocessed by making a 3D stack, and normalizing, resizing, and completing the stack was performed. A 3D-CNN model architecture was designed to train and test preprocessed images. We achieved an accuracy of 93.12 % when 2D axial slices were used. When the 2D slices of a patient were stacked to form a 3D image, an accuracy of 85.71 % was achieved on the test set. Overall, the 3D-CNN model performed very well on the test set. The earliest and the most accurate diagnosis from computational imaging methods can help reduce the huge burden of cSVD and its associated morbidity in the form of vascular dementia.

Japanese persimmon (Diospyros kaki) are a good source of vitamins A and C and dietary fiber, while also being low in calories and fat. These fruits are popular in many Asian cuisines and are enjoyed in various ways, including fresh, dried, or cooked in multiple value-added products. This publication will provide information on persimmon phenological growth stages of four established varieties grown at the UF/IFAS North Florida Research and Education Center in Quincy, Florida. The nine major persimmon growth stages are divided into two categories: vegetative and reproductive stages. This publication provides illustrative figures for each development stage. Accurate identification of phenological stages is important for making informed decisions on plant care, fertilizer, irrigation, and fruit harvesting. This information will be helpful to students, growers, homeowners, nursery workers, and Extension agents interested in growing persimmons in Florida.

With the rising prevalence of chronic liver disease worldwide, the incidence and prevalence of acute-on-chronic liver failure (ACLF) are increasing and attribute to higher morbidity, mortality, and healthcare costs. Many of such patients die without being considered for the lifesaving treatment option of liver transplantation. The underutilization of liver transplantation as a therapeutic option in the setting of ACLF, is due to multiple reasons; with the heterogeneity of ACLF and the lack of universal definition being the key players. Liver transplantation listing and allocation are based on MELD score. As of now, we do not know where MELD score stands in regard to defining ACLF and the prognostication of such patients. This insight is very important for the efficient identification of potential liver transplantation candidates in the setting of ACLF. This review paper investigates the role of liver transplantation in the setting of ACLF. In light of recent evidence, MELD score is not the perfect model in the setting of ACLF either. The safety of liver transplantation, either deceased donor or living donor, among ACLF patients has been debated. The short-term mortality rate of ACLF patients has created a need for a standard liver transplant selection criterion for these patients. Based on published literature, we find that three commonly used ACLF definitions may be used in combination to define the sensitivity, specificity, and futility of ACLF and we propose an algorithm to best identify patients for urgent liver transplantation in the setting of ACLF. Moreover, we discuss the data on the safety of liver transplantation in the setting of ACLF. Future validation of this multifaceted approach could bridge the gap between ACLF patients and appropriately guided medical intervention.