International Islamic University, Islamabad

We reviewed with interest the recent publication by Ko et al., which demonstrated that a higher baseline albumin–bilirubin (ALBI) grade (≥ 2) independently predicts poorer long-term survival in patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE), including those with preserved liver function classified as Child–Pugh A [1]. The extended follow-up duration and comprehensive multivariate analyses strengthen the authors' conclusion that ALBI serves as an objective and clinically relevant prognostic indicator in this population. The prognostic relevance of baseline ALBI grade in TACE-treated HCC has been consistently supported by evidence from large cohorts. A meta-analysis including over 6500 patients confirmed that higher pretreatment ALBI grades are significantly associated with reduced overall survival, reinforcing its value as a standardized marker of hepatic functional reserve [2]. In contrast to the Child–Pugh classification, ALBI is based exclusively on serum albumin and bilirubin, thereby minimizing interobserver variability. Although baseline ALBI allows effective pretreatment risk stratification, liver function is dynamic and may change substantially following TACE. Increasing evidence suggests that the deterioration of ALBI grade after the initial TACE session is associated with earlier recurrence and reduced recurrence-free survival, reflecting treatment-related hepatic injury and declining liver reserve [3]. Furthermore, the progressive worsening of ALBI grade with repeated TACE procedures has been linked to reduced survival outcomes, underscoring the limitations of relying exclusively on single baseline measurements [4]. These findings demonstrate that serial assessment of ALBI may offer additional prognostic value beyond pretreatment evaluation alone. Integration of ALBI with established clinical staging systems may further improve risk stratification. Treatment decision-making in HCC frequently integrates liver function assessment alongside the Barcelona Clinic Liver Cancer (BCLC) staging system. Several studies have demonstrated that prognostic models combining ALBI with tumor burden and performance status outperform either parameter alone in predicting outcomes following TACE [5]. Additionally, the recently proposed simplified ALBI score (EZ-ALBI) has shown effective prognostic discrimination in intermediate-stage HCC, with performance comparable to or exceeding that of conventional scoring indices [6]. Prospective validation of ALBI-integrated staging models may enhance individualized treatment selection and inform optimal timing of therapeutic transitions. Methodological limitations should be acknowledged. Most studies assessing ALBI, including the analysis by Ko et al. [1], are retrospective and therefore subject to selection bias, heterogeneity in TACE techniques, and variability in post-treatment management. Prospective, multicenter studies are needed to validate optimal ALBI thresholds, standardize definitions of dynamic ALBI change, and confirm applicability across diverse clinical settings. Beyond its prognostic role, an important unresolved issue is whether ALBI can be used to actively guide treatment sequencing. Early identification of patients exhibiting post-TACE deterioration in ALBI grade may allow a timely transition to systemic therapies, potentially preserving hepatic function and improving outcomes. Prospective interventional trials evaluating ALBI-guided therapeutic strategies are therefore necessary to clarify its role in clinical decision-making and guideline development. In conclusion, although the baseline ALBI grade is a robust and objective prognostic marker for patients with HCC undergoing TACE, the integration of dynamic ALBI changes, incorporation with established staging systems, and validation of these approaches prospectively may further enhance its clinical utility and support more personalized management strategies. The authors have nothing to report. The authors have nothing to report. The authors declare no conflicts of interest. No new data were generated for this research.

Human communication has also grown beyond face to face and computer-based interaction to include communication with machines with the fast development of conversational artificial intelligence. Such a change has brought up an issue regarding the way in which chatbots formulate politeness, interpersonal tone, and gendered linguistic behavior. The literature has discussed the bias against gender in AI systems, politeness in human-AI interaction, and how AI training data affect stereotypical responses, but there is a relative paucity of studies that explicitly compare the linguistic response of AI chatbots to male-coded and female-coded users. This paper fills that gap by examining lexical, pragmatic, and politeness-based differences in responses that were produced by various versions of ChatGPT. Based on the Politeness Theory by Brown and Levinson (1987) and the Gender and Language theory by Holmes (1995), the study will examine the hypothesis on whether AI replicates gender tendencies in positive/negative politeness, mitigation, hedging, and conversational style. Qualitative design was applied in which gender-coded prompts were then entered into ChatGPT and the results analyzed using word-frequency, contextual interpretation, and thematic comparison with politeness-marketing coded responses. The results demonstrate apparent differences: prompts marked as male were approached more directly, more concisely, and task-focused, whereas prompts marked as female tend to get much warmer and richer in detail, including more occurrences of positive politeness and indirectness. These tendencies imply that AI chatbots fail to assume an apolitical communicative position but rather reflect culturally transmit-ted gender standards that are represented in their training material. This research paper will add to sociolinguistic literature on non-human communicators and will indicate the necessity of more equitable, stereotype-free AI language systems.

Aim: Meteorological factors pose a significant threat to food security through disruptions in food availability, access, utilization, and stability.Extreme temperatures, erratic precipitation patterns, and extreme weather events adversely affect agricultural productivity, fisheries, and livestock, exacerbating inequalities in food access.This study examines the impact of meteorological factors on food security in Pakistan using the Food Insecurity Experience Scale (FIES) and investigates how household-level adoption of Information and Communication Technology (ICT) mitigates these effects across different climatic zones. Material and methods: Using district-level climatic data from NASA Power and household-level ICT adoption indicators sourced from the Pakistan Social and Living Standards Measurement 2019-2020 survey, the study examines the impact of climate factors on food security and examines the moderating effect of ICT. The estimation is done at the national and climatic zones utilizing linear (OLS) and quantile regression. Moreover, the direct and indirect effects of meteorological factors are calculated, and the moderating role of ICT is computed.Results: The findings reveal that all four meteorological variables negatively and significantly affect household food security.Among these, wind speed and precipitation exert the strongest adverse effects, particularly in tropical zones.The results also confirm regional heterogeneity, with the tropical zone being the most vulnerable.The quantile regression shows that the mitigation through ICT is most effective for households with a low level of food security.

India’s SSBN program does not bode well for the security calculus of the Asia-Pacific region. The security architecture of South Asia is fundamentally different from that of the Cold War era. During the Cold War, the two competing powers, the U.S. and the USSR, were geographically distant from each other. This lack of proximity between their strategic centers reduced the chances of crisis instability, miscalculation, and escalation crisis. In contrast, South Asia’s geographic contiguity, particularly between India and Pakistan, places their strategic centers in close proximity, significantly increasing the risk of crisis instability. India’s sea-based deterrent poses serious concerns for the region for two main reasons. First, the independent command and control system of SSBNs raises the issue of pre-delegation, especially if communication between the SSBNs and political leadership on land breaks down during a crisis. In such a scenario, it is unclear whether the SSBNs’ autonomous command and control system would be capable of accurately interpreting the situation. Second, India’s SSBNs, armed with sea-launched ballistic missiles (SLBMs) with intercontinental ranges, not only trigger an arms race within South Asia but may escalate strategic competition beyond the region. This research paper tries to critically discuss India’s sea-based deterrence under the shadow of its touted no-first-use policy. India’s doctrinal shift from countervalue second strike to counterforce stance, considering its SSBNs development. Moreover, the ranges and capability of India’s SLBMs will be discussed through the lens of India’s pursuit of prestige, and global strategic ambitions.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder linked with oxidative imbalance, cholinergic dysfunction and neuroinflammation, necessitates developing new multitarget natural compounds with potential disease-modifying action. Piperitone was evaluated using in-silico, in-vitro and in-vivo methods. In-silico study identified the pharmacokinetic parameters (PK) and the interaction stability of piperitone with acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and β-secretase. In-vivo assessment of spatial memory in scopolamine-induced rat model was identified by behavioral assays with donepezil as a reference standard. In-vitro assays identified activity of cholinesterases, oxidative stress markers, levels of antioxidants and neuroinflammatory substrates, quantified with Reverse Transcription Polymerase Chain Reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA). Piperitone demonstrated favorable PK properties & docking scores comparable to Donepezil, Tacrine & QUD. Molecular dynamics simulations (MDS) confirmed stable associations with catalytic residues of cholinesterases and beta-secretase. Dose dependent reduction was recorded in cholinesterases, improvement in behavioral outcomes, and supplemented defenses of antioxidants including Glutathione (Reduced Form (GSH), Glutathione S-Transferase (GST), Catalase (CAT), Superoxide Dismutase (SOD), and diminished Lipid Peroxidation (LPO), Nitric Oxide (NO), Tumor Necrosis Factor-alpha (TNF-α), Interleukin (IL)-1β, IL-18, Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-κB), NOD-like Receptor Family Pyrin Domain Containing 3 (NLRP3) and amyloid-β production, while improving Nuclear factor erythroid 2-related factor 2 (Nrf2) signaling. Piperitone showed significant neuroprotective and cognitive enhancement benefits by modulating cholinergic signaling, oxidative stress, and neuroinflammation. These multitarget actions advocate piperitone as a prospective lead candidate for the development of disease modifying treatments for AD.

The Marshall–Olkin–Gompertz (MOG) distribution extends the classical Gompertz model through the Marshall–Olkin family, introducing an additional parameter that enhances flexibility in lifetime and reliability modeling. Its probability density, cumulative, survival, and hazard functions are derived and analyzed. Graphical studies show that the MOG model can represent increasing, decreasing, or bathtub shaped hazard rates, making it suitable for diverse real world data. Parameter estimation is performed using the maximum likelihood method. Overall, the MOG distribution provides a versatile generalization of the Gompertz law, offering improved adaptability in engineering, biological, and actuarial applications.

Abstract Induction motors play a critical role in industrial operations and electric vehicles, yet their reliability is often compromised by stator winding inter-turn short-circuit (ITSC) faults. To mitigate costly downtimes, this study investigates machine learning-based fault detection methods, comparing Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, and Physics-Informed Neural Networks (PINNs). A benchmark dataset comprising stator current signals, leakage flux, and varying load conditions was employed to train and evaluate the models. While classification across seven fault categories remained challenging, the proposed framework achieved outstanding results in binary classification (healthy vs. faulty). In particular, the LSTM model attained a near-perfect accuracy of 100%, and the PINN model achieved 99.32%, both surpassing the baseline ANN performance of 99.01%. These findings highlight the effectiveness of temporal modeling with LSTM and the added value of incorporating motor physics into PINNs, especially in scenarios with limited data. The results confirm that advanced machine learning models can significantly improve early fault detection, enabling more reliable predictive maintenance and reduced operational costs in industrial systems.