Abstract Klebsiella pneumoniae  is one of the leading causes of urinary tract infections (UTIs) and is increasingly associated with multidrug resistance, posing significant therapeutic challenges. Continuous surveillance of its antimicrobial susceptibility profile is essential for effective clinical management. This cross-sectional study was conducted from July 2023 to December 2023 in a tertiary care hospital. A total of 12 urine samples were collected from patients with suspected UTIs. Samples were cultured using standard microbiological techniques, and isolates were identified by colony morphology, Gram staining, and biochemical tests. Antibiotic susceptibility testing was performed by the Kirby–Bauer disc diffusion method following CLSI guidelines. Out of 12 urine samples, 10 (83%) showed bacterial growth and were identified as Klebsiella pneumoniae . Among the isolates, 8 (80%) were from female patients and 2 (20%) from male patients. The majority of patients (40%) belonged to the 41–60 years age group, followed by ≥61 years (30%). Most isolates were obtained from OPD patients (70%), while 30% were from IPD patients. The highest sensitivity was observed to Meropenem (90%) and Tigecycline (90%), followed by Gentamicin (70%). Moderate sensitivity (50%) was noted for Cefotaxime, Amoxicillin, Ceftazidime, Cefoxitin, Ampicillin, and Ciprofloxacin. The lowest sensitivity was seen with Doxycycline (10%) and Colistin (40%). Multidrug resistance (MDR) was detected in 7 (70%) isolates, while no extensively drug-resistant (XDR) or pan drug-resistant (PDR) strains were identified. Klebsiella pneumoniae  remains a significant uropathogen with a high prevalence of multidrug resistance. Carbapenems and Tigecycline showed the highest effectiveness, whereas resistance to commonly used antibiotics was considerable. Regular antimicrobial surveillance and rational antibiotic use are essential to control the spread of resistant strains.

Abstract This study applies the Small Baseline Subset InSAR (SBAS-InSAR) technique, together with an Analytic Hierarchy Process (AHP)-GIS weighted-overlay framework, to assess surface deformation and landslide susceptibility along the Ghazi-i-Millat Road (GMR), Azad Jammu and Kashmir, Pakistan. Time-series analysis of 91 Sentinel-1 acquisitions (January 2021-December 2023) indicates a mean Line-of-Sight (LOS) velocity ranging from -14.50 to +15.00 mm/year, with a maximum cumulative LOS displacement of approximately +130 mm and -96 mm over the three-year observation period; a localized maximum LOS velocity of 17.07 mm/year was also recorded. Because LOS displacement records motion toward or away from the sensor rather than true vertical or slope-parallel movement, these values are interpreted as indicators of possible ground instability rather than direct proof of active landsliding. Landslide susceptibility mapping incorporates eight causative parameters, slope gradient, slope aspect, geology, drainage network, fault proximity, road proximity, NDVI, and NDWI, combined through AHP-derived weights in a GIS weighted-overlay model. Dense vegetation cover is associated with qualitatively reduced SBAS-InSAR coherence and data reliability, consistent with well-documented vegetation-induced radar decorrelation. The weighted-overlay analysis assigns the greatest relative importance to slope gradient, followed by drainage network and road proximity, and the resulting susceptibility map places the majority of the mapped landslide inventory within the considerable-to-very-high susceptibility classes. The findings support the value of combining SBAS-InSAR deformation monitoring with AHP-GIS susceptibility mapping as a regional decision-support tool for infrastructure planning, slope stabilization, and drainage management along this economically important corridor, while underscoring the need for independent field validation, ascending/descending geometric decomposition, and quantitative uncertainty assessment in future work.

Abstract Microbial communities persist under recurrent abiotic and biotic disturbance, yet the organizational basis of this persistence remains incompletely explained. Existing frameworks describe resistance, resilience, functional redundancy, social evolution, and ecological networks, but do not explicitly define how demonstrated protective effects are distributed among community members or whether this distribution predicts stability beyond diversity, composition, and functional potential. Here, we propose the Collective Ecological Protection Theory (CEPT), a systems-level framework in which stability is influenced by a protective architecture: the directed, weighted, disturbance- and response-specific network through which the activity of a provider increases the persistence or functional performance of a recipient relative to a matched counterfactual. CEPT separates protective mechanisms from their organization and identifies six candidate descriptors: protective coverage, protective redundancy, mechanistic complementarity, spatial reach, modularity, and dependency concentration. Protection is context dependent and may be weakened by spatial separation, correlated failure, costly cooperation, exploitation, or dependence on a small number of providers. We derive four discriminating predictions based on matched-community rewiring, targeted provider loss, independent protection, and architectural reconstruction during recovery. CEPT receives support to the extent that independently measured protective architecture improves causal or out-of-sample prediction of a specified stability response beyond established compositional, functional, and environmental factors. It therefore offers a testable organizational explanation for how distinct protective processes combine to influence microbial resistance, persistence, recovery, and maintenance of function. By identifying distributed protection and single points of failure, CEPT may also provide a mechanistic basis for assessing ecological vulnerability and risk in disturbed microbial systems.

Abstract Gold-mineralized grasslands may contain spatially variable gold (Au) and associated elements such as arsenic (As) and mercury (Hg). Ecological effects are difficult to attribute because natural mineralization, mining disturbance and other sources of metal contamination may overlap. This narrative review examines vegetation, soil geochemistry and microbial enzyme activity in grasslands and related metal-affected systems. The available studies are heterogeneous and often do not measure Au, vegetation, soil properties and enzyme activity together. Studies that do not measure Au are treated as contextual evidence, not direct evidence of Au-specific effects. Unsupported cross-site Au concentrations, pooled regolith statistics, universal particle-size or dispersion ranges, plant accumulation values and enzyme diagnostic thresholds have been removed. Enzyme activities are complementary indicators of soil biological functioning, not validated Au-specific diagnostics. Restoration evidence from other ecosystems can inform hypotheses but cannot establish universal restoration outcomes for naturally mineralized grasslands. Changes in enzyme activity or SOC alone do not demonstrate net-zero performance; assessment requires repeated SOC-stock measurements, plant carbon inputs, CO₂, CH₄ and N₂O fluxes, and consideration of restoration-related emissions. The review therefore presents a conceptual monitoring agenda and testable research priorities rather than a validated causal model or universal thresholds.

Abstract Freshwater scarcity is a persistent challenge for coastal households in south-eastern Bangladesh, where salinity intrusion, seasonal hydrological variability, cyclones, and competing water demands can constrain domestic water use and livelihood activities. Guided by the Sustainable Livelihoods Framework (SLF), this study was designed to compare natural, physical, human, financial, and social livelihood capitals across coastal, peri-coastal, and inland-transition settings in the Chattogram–Cox’s Bazar region. The author confirms that 384 households completed the questionnaire, alongside qualitative data collection described in the original manuscript. However, the zone-specific sample sizes and numerical results cannot be verified because the household-level dataset and original statistical output are unavailable. Consequently, this revision does not report unverified zone means, index scores, regression coefficients, subgroup percentages, or significance tests. The cross-sectional design can support descriptive associations only and cannot establish causation. The findings must be reconstructed from verifiable records or the affected numerical claims removed before submission.