Search

Abstract Antimicrobial resistance (AMR) poses a significant global health threat, primarily stemming from its misuse and overuse in both veterinary and public healthcare systems. The consequences of AMR are severe, leading to more severe infections, increased health protection costs, prolonged hospital stays, unresponsive treatments, and elevated fatality rates. The impact of AMR is direct and far-reaching, particularly affecting the Sustainable Development Goals (SDGs), underscoring the urgency for concerted global actions to achieve these objectives. Disproportionately affecting underprivileged populations, AMR compounds their vulnerabilities, pushing them further into poverty. Moreover, AMR has ramifications for food production, jeopardizing sustainable agriculture and diminishing the livelihoods of farmers. The emergence of antibiotic-resistant bacteria in underprivileged areas heightens the risk of complications and mortality. Climate change further contributes to AMR, as evidenced by increased instances of foodborne salmonellosis and the development of antibiotic resistance, resulting in substantial healthcare costs. Effectively addressing AMR demands collaboration among governments, entrepreneurs, and the public sector to establish institutions and policies across all regulatory levels. Expanding SDG 17, which focuses on partnerships for sustainable development, would facilitate global antimicrobial stewardship initiatives, technology transfer, surveillance systems, and investment in vaccine and drug research. The World Bank's SDG database, tracking progress towards sustainable development, reveals a concerning picture with only a 15% success rate till 2023 and 48% showing deviation, underscoring a global gap exacerbated by the COVID-19 pandemic. Tackling AMR's global impact necessitates international cooperation, robust monitoring, and evaluation methods. The five priorities outlined guide SDG implementation, while impoverished countries must address specific challenges in their implementation efforts. Addressing AMR and its impact on the SDGs is a multifaceted challenge that demands comprehensive and collaborative solutions on a global scale.

Background and Aim: The dearth of new antibiotics necessitates alternative approaches for managing infections caused by resistant superbugs. This study aimed to evaluate the lytic potential of the purified bacteriophage PKp-V1 against extended-spectrum β-lactamase (ESBL) harboring hypervirulent Klebsiella pneumoniae (hvKp)-K1 recovered from veterinary specimens. Materials and Methods: A total of 50 samples were collected from various veterinary specimens to isolate K. pneumoniae, followed by antimicrobial susceptibility testing and molecular detection of various virulence and ESBL genes. Multilocus sequence typing of the isolates was performed to identify prevalent sequence types. The bacteriophages were isolated using the double-agar overlay method and characterized using transmission electron microscopy, spot tests, plaque assays, stability tests, and one-step growth curve assays. Results: Among 17 (34%) confirmed K. pneumoniae isolates, 6 (35%) were hvKp, whereas 13 (76%) isolates belonging to the K1 type were positive for the wzy (K1) virulence gene. All (100%) hvKp isolates exhibited the allelic profile of ST258. Overall, PKp-V1 exhibited an 88 % (15/17; (p ≤ 0.05) host range, among which all (100 %; p ≤ 0.01) hvKp isolates were susceptible to PKp-V1. PKp-V1 exhibited a lytic phage titer of 2.4 × 108 plaque forming unit (PFU)/mL at temperatures ranging from 25°C to 37°C. The lytic phage titers of PKp-V1 at pH = 8 and 0.5% chloroform were 2.1 × 108 PFU/mL and 7.2 × 109 PFU/mL, respectively. Conclusion: Although the incidence of ESBL-infected K. pneumoniae in veterinary settings is worrisome, PKp-V1 phages showed considerable lytic action against the host bacterium, indicating the potential of PKp-V1 as a possible alternative therapeutic option against MDR K. pneumoniae.

Abstract Chikungunya virus (CHIKV) is a single-stranded RNA virus belonging to the genus Alphavirus and is responsible for causing Chikungunya fever, a type of arboviral fever. Despite extensive research, the pathogenic mechanism of CHIKV within host cells remains unclear. In this study, an in-silico approach was used to predict that CHIKV produces micro-RNAs that target host-specific genes associated with host cellular regulatory pathways. Putative micro-RNAs of CHIKV were predicted using the miRNAFold and Vmir RNA structure web servers, and secondary structure prediction was performed using RNAfold. Host-specific target genes were then predicted, and hub genes were identified using CytoHubba and module selection through MCODE. Functional annotations of hub genes revealed their association with various pathways, including osteoclast differentiation, neuroactive ligand-receptor interaction, and mRNA surveillance. We used the freely available dataset GSE49985 to determine the level of expression of host-specific target genes and found that two genes, F-box and leucine-rich repeat protein 16 (FBXL16) and retinoic acid receptor alpha (RARA), were down-regulated, while four genes, RNA binding protein with serine-rich domain 1 (RNPS1), RNA helicase and ATPase (UPF1), neuropeptide S receptor 1 (NPSR1), and vasoactive intestinal peptide receptor 1 (VIPR1), were up-regulated. These findings provide insight into novel miRNAs and hub genes associated with CHIKV infection and suggest potential targets for therapeutic intervention. Further experimental validation of these targets could lead to the development of effective treatments for CHIKV-mediated diseases.

Abstract Climate change, caused by both natural and human activities, poses significant threats to global health, including rising temperatures, extreme weather events, and environmental disruptions. The One Health concept, emphasizing the interconnectedness of human, animal, and environmental health, is crucial in addressing these challenges. Climate change is linked to the emergence and resurgence of infectious diseases, resulting in substantial economic losses worldwide due to outbreaks, floods, and disrupted trade, among other factors. Therefore, it is crucial to adapt to this changing climate to safeguard One Health. By implementing a One Health approach, we can effectively address the consequences of climate change and make substantial contributions to health and food safety. This approach also holds the potential to mitigate economic losses, particularly in low and middle-income countries. Additionally, in the future, research interventions based on the One Health framework may offer sustainable solutions to combat climate change.

Rheumatoid arthritis (RA) is associated with comorbidities like dyslipidemia and liver damage. Acorus calamus L. is commonly used to treat inflammatory and hepatorenal ailments. Herein, we assessed the hypolipidemic and hepatoprotective activities of A. calamus rhizome extracts in adjuvant-induced arthritis (AIA) in rats. A. calamus rhizome’s methanol (AME) and n-hexane (AHE) extracts were prepared. In vivo, fifty-four female Wistar rats were allocated into nine groups (n = 6). The AIA was induced by injecting Complete Freund’s adjuvant (100 µL, s.c.) into the rat’s right hind paw. After AIA induction, animals were orally treated with meloxicam (MEL; 3 mg/kg) and graded doses (125, 250, and 500 mg/kg) of AME and AHE for 28 consecutive days. Results showed that both plant extracts dose-dependently restored the body weight and liver index of arthritic rats. Each plant extract significantly (p

Objective(s): This study focused on the evaluation of antioxidant and antidiabetic activities of polyherbal extract (PHE), containing Cassia absus (L.), Gymnema sylvestre (R. Br.), Nigella sativa (L.), and Piper nigrum (L.), in alloxan-induced diabetes model.Materials and Methods: In vitro, HPLC characterization, DPPH scavenging assay, and α-amylase inhibition test were conducted. In vivo, acute oral toxicity of PHE was assessed. Alloxan-induced diabetic Wistar rats (n=6) were orally treated with PHE (200, 400, and 600 mg/kg/day) and glibenclamide (GLB; 10 mg/kg/day) for six consecutive weeks. Then, biochemical biomarkers, oxidative stress parameters, histopathological examination, and mRNA expression levels (RT-qPCR) were determined.Results: The presence of polyphenols in PHE was confirmed in correlation to marked DPPH scavenging (IC50: 1.60 mg/ml) and α-amylase inhibition (IC50: 0.82 mg/ml). PHE demonstrated no toxicity in rats up to a dose of 2000 mg/kg. In diabetic rats, PHE dose-dependently ameliorated the serum levels of glucose, insulin, glycated hemoglobin A1c (HbA1c), leptin, and glucokinase (GCK). Also, PHE substantially alleviated serum inflammatory markers (TNF-α and CRP) and oxidative stress indicators (MDA, SOD, and CAT) in pancreatic tissues. PHE, particularly at 600 mg/kg, attenuated cellular oxidative stress via modulating the mRNA expression levels of genes regulating MAPK/JNK (Mapk-8, Traf-4, and Traf-6) and Nrf-2/Keap-1 pathways and promoted insulin signaling through up-regulating insulin signaling cascade (Pdx-1, Ins-1, and Ins-2), as compared to GLB. Furthermore, histopathological findings supported the aforementioned results.Conclusion: Our study suggests that polyherbal extract has promising antioxidant and antidiabetic activities by modulating the MAPK/JNK, Nrf-2/Keap-1, and insulin signaling pathways.

Effects of polyherbal mixture on hepatorenal injury and dyslipidaemia in alloxan-induced diabetic rats were evaluated. Alloxan monohydrate (120 mg/kg, i.p.) was injected into Wistar rats to induce diabetes. Animals were allotted to six groups (n = 6) and treatments were administered for consecutive six weeks as normal and diabetic controls, glibenclamide and polyherbal mixture (200, 400 and 600 mg/kg), respectively. Ameliorative effects of the polyherbal mixture were investigated by assessing FBG levels, changes in body and organ (liver and kidney) weights, biochemical parameters, oxidative stress along with inflammatory parameters in addition to histopathological examination of the liver and kidney. The results showed that alloxan-injected rats had significant (P

Background and Objectives: Staphylococcus aureus is a prominent component of the human flora; however, it can cause various pathological conditions. The emergence of methicillin-resistant S. aureus (MR-SA) has been significantly influenced by the overuse and inappropriate administration of antibiotics. The frequency of MR-SA nasal colonization among healthcare workers (HCWs) is increasing, and MR-SA is not restricted to hospital settings, with a notable rise in infections among individuals unrelated to HCWs. This study aimed to assess the prevalence of S. aureus nasal carriage among students at Government College University Faisalabad (GCUF), University of Agriculture Faisalabad (UAF), a Government School (GS), and a Private School (PS) to characterize the phenotypic traits of isolates and evaluate antimicrobial resistance profiles. Materials and Methods: A total of 1200 nasal swabs were inoculated on blood and mannitol salt agar, followed by phenotypic identification of S. aureus and MR-SA using biochemical tests. Antimicrobial susceptibility testing was conducted via the Kirby–Bauer disk diffusion method, and minimum inhibitory concentration (MIC) determination was performed using the broth dilution method. Additionally, nuc and mecA gene amplification through PCR aided in isolate identification. Results: The results revealed that 14% (168) of students harbored S. aureus in their nasal cavities, with 8.5% (102) carrying methicillin-sensitive S. aureus (MSSA) and 5.5% (66) carrying MR-SA. Male students exhibited higher S. aureus (57.7%) and MR-SA (21.4%) prevalence compared to females (42.3% and 17.9%, respectively). Urban students showed a higher S. aureus prevalence (54.2%), while rural students exhibited a higher MR-SA rate (22%). Overall, 80.3% of S. aureus isolates displayed resistance to erythromycin followed by fluoroquinolones (47.6%) and clindamycin (42.2%). All the S. aureus isolates, including MR-SA, remained susceptible to vancomycin and linezolid. PCR results revealed that 95.5% (63) of MR-SA isolates carried the mecA gene. Conclusions: The high prevalence of multi-drug-resistant (MDR) S. aureus raises significant public health concerns, with educational institutions potentially serving as reservoirs for bacterial transmission. The improper use of antibiotics contributes to bacterial resistance and increased infection rates. It is crucial to implement measures to prevent antibiotic misuse and develop comprehensive strategies within educational settings to effectively combat S. aureus and MR-SA prevalence.

Abstract Liver’s contribution to innate immunity is eminent. However, uncontrolled inflammatory conditions predispose the liver to immune-mediated injury. Nigella sativa L. is traditionally implicated in infectious, inflammatory, metabolic and hepatorenal complications. This study aimed to evaluate the protective role of N. sativa seed extract (NSE) against concanavalin A (ConA)-induced acute immunological liver injury in mice. In vitro, NSE was subjected to quantitative phytochemical characterization and 1,1-diphenyl-2-picryhydrazyl (DPPH) analysis. In vivo, male Balb/c mice were pretreated with NSE (100, 200 and 400 mg/kg/day, p.o.) and pioglitazone (5 mg/kg/day, p.o.) for seven consecutive days. A single dose of ConA (12 mg/kg, i.v.) was injected and samples were collected for biochemical, histopathological and qRT-PCR analyses after 8 h of ConA injection. In vitro analysis showed considerable quantities of polyphenols and significant DPPH scavenging ability of NSE. In mice, ConA resulted in a significant (p

The dissemination of resistant pathogens through food supply chains poses a significant public health risk, spanning from farm to fork. This study analyzed the distribution of Shiga toxin-producing Escherichia coli (STEC) across various sources within the animal-based food supply chain. A total of 500 samples were collected from livestock, poultry, the environment, fisheries, and dairy. Standard microbiological procedures were employed to isolate and identify E. coli isolates, which were further confirmed using MALDI-TOF and virulence-associated genes (VAGs) such as stx1, stx2, ompT, hylF, iutA, fimH, and iss. The phenotypic resistance patterns of the isolates were determined using the disc diffusion method, followed by molecular identification of antibiotic resistance genes (ARGs) through PCR. STEC were subjected to PCR-based O typing using specific primers for different O types. Overall, 154 (30.5%) samples were confirmed as E. coli, of which 77 (50%) were multidrug-resistant (MDR) E. coli. Among these, 52 (67.53%) isolates exhibited an array of VAGs, and 21 (40.38%) were confirmed as STEC based on the presence of stx1 and stx2. Additionally, 12 out of 52 (23.07%) isolates were identified as non-O157 STEC co-harbouring mcr-1 and blaNDM-1. O26 STEC was found to be the most prevalent among the non-O157 types. The results suggest that the detection of STEC in food supply chains may lead to serious health consequences, particularly in developing countries with limited healthcare resources.

Global climate issues are a serious impediment to green and sustainable urban development. The purpose of this study is to explore the influence of structural change on urbanization in terms of population, energy, industry, and economics while accounting for per capita carbon emissions. In this article, three income groups are defined using the World Bank database's income criteria, and panel data for 132 countries and three income groups is regressed using a panel threshold model from 1997 to 2021. The threshold variable in this framework is carbon emissions per capita, and the explained variable is urbanization; the explanatory factors are population structure (newborn birth rate), energy structure (renewable energy), industry structure (industrialization), and economic structure (GDP). The study's findings revealed that: (1) the newborn birth rate and renewable energy inhibit urbanization; however, the mediating effect of per capita carbon emissions mitigates this phenomenon. (2) The impact of industrialization on urban development has gradually become positive, mediated by per capita carbon emissions. (3) The mediating effect of per capita carbon emissions has increased economic growth's contribution to urbanization. (4) For high-, middle-, and low-income groups, there is some variability in the non-linear connection between the variables; in particular, the association between the newborn birth rate, renewable energy, and urbanization is more substantial. Finally, based on the study's findings, important policy suggestions in terms of structural determinants and heterogeneity are presented for policymakers all around the globe to use as a guide for implementing global urban-environmentally sustainable development plans.

AbstractEarthquake risk in urban areas has recently taken quite a lot of attention. Risk is a cross-cutting combination of vulnerability and hazard. Abbottabad district, a rapidly growing district, is an earthquake-prone area in the north of Pakistan. In 2005, the region suffered enormous casualties due to a significant earthquake (Mw = 7.6). The various regional behaviours, including social, physical, and economic behaviours, are causing a vulnerable situation. At the same time, the high seismicity has the potential to invite earthquake hazard, thus ensuing a possible risk situation in the region. Consequently, this study proposes an integrated approach involving an artificial neural network (ANN), convolutional neural network (CNN), and analytic network process (ANP) to carry out the seismic risk analysis of the Abbottabad district. The proposed two integrated frameworks are ANP-ANN and ANP-CNN. This kind of methodology has not been applied before in this area. The geographic information system is used to formulate a broad range of contributing factors depicting the hazard and vulnerability of the area and potentially contributing to the seismic risk. A total of 16 contributing factors are chosen for this study. The selected factors are weighed using the ANP, and training and testing databases are generated to train and test the models, thus producing the area’s earthquake risk maps (ERMs). As per the ERMs, the northwestern regions and the borderline regions from the southeast, covering an area of 30%, possess a greater earthquake risk. The rest of the 70% of the area has less risk exposure. The AUC values of ANN and CNN are found to be 0.843 and 0.878, respectively, exhibiting a good performance by the models. The study paves the way for the concerned authorities to match and redefine their policies for developing, expanding, and managing the region’s structural, health care, and disaster management facilities.

Owing to the climatic dryness and shortage of surface water, Pakistani farmers have been forced to rely on groundwater resources to meet the irrigation demand for crops. Pumping an excessive amount of water from the aquifers is turning it brackish. Our study aims to select a suitable depth of freshwater for installing tube wells in a study area, Tehsil Nankana Sahib. Sixteen vertical electrical soundings (VES) were arrayed in this study area, and water samples were also collected to record their electrical conductivity. The interpolation maps for different parameters were generated by using the inverse distance weighted interpolation method. The obtained results depict that the central and southeastern region has good groundwater at depths from 60 to 90 m, the northeastern region has marginally fit water, while the northwestern region is marked as having an unfit groundwater zone. This research article is not only a case study but also can be used for other areas of Punjab, Pakistan, facing similar groundwater problems. However, the comparison of VES results with borehole data is critical to achieve better accuracy. An extensive exploratory borehole study of the area is recommended to further characterize the subsurface lithology.

Accurate estimation of planetary greenhouse gas (GHG) emissions at the scale of individual emitting activities is a critical need for both scientific and policy applications. Powerplants represent the single largest and most concentrated form of global GHG emissions. Climate Trace, co-founded and promoted by former U.S. Vice President Al Gore, is a new effort using, in part, artificial intelligence (AI) approaches to estimate asset-scale GHG emissions. Climate Trace recently released a database of global powerplant CO _2 emissions at the facility-scale that uses both AI and non-AI estimation approaches. However, no independent peer-reviewed assessment has been made of this important global emissions database. Here, we compare the Climate Trace powerplant CO _2 emissions to an atmospherically calibrated, multi-constraint estimate of powerplant CO _2 emissions in the United States. The 3.7% (65) of compared facilities that used an AI-based approach show a mean relative difference (MRD) of −1.1% (SD: 46.4%) in the year 2019. The 96.3% (1726) of the facilities that used a non-AI-based approach show a MRD of −50.0% (SD: 117.7%). Of the non-AI estimated facilities, 151 (8.7%) facilities agree to within ±20%. The large differences between Climate Trace and Vulcan-power emission estimates for these facilities is primarily caused by Climate Trace’ use of a national-mean power plant capacity factor (CF) which is a poor representation of the reported power plant CFs of individual US facilities and leads to very large errors at those same 1726 facilities.

The aim of this study was to provide a comparative analysis of chitosan (CH), copper oxide (CuO), and chitosan-based copper oxide (CH-CuO) nanoparticles for their application in the healthcare sector. The nanoparticles were synthesized by a green approach using the extract of Trianthema portulacastrum. The synthesized nanoparticles were characterized using different techniques, such as the synthesis of the particles, which was confirmed by UV–visible spectrometry that showed absorbance at 300 nm, 255 nm, and 275 nm for the CH, CuO, and CH-CuO nanoparticles, respectively. The spherical morphology of the nanoparticles and the presence of active functional groups was validated by SEM, TEM, and FTIR analysis. The crystalline nature of the particles was verified by XRD spectrum, and the average crystallite sizes of 33.54 nm, 20.13 nm, and 24.14 nm were obtained, respectively. The characterized nanoparticles were evaluated for their in vitro antibacterial and antibiofilm potential against Acinetobacter baumannii isolates, where potent activities were exhibited by the nanoparticles. The bioassay for antioxidant activity also confirmed DPPH scavenging activity for all the nanoparticles. This study also evaluated anticancer activities of the CH, CuO, and CH-CuO nanoparticles against HepG2 cell lines, where maximum inhibitions of 54, 75, and 84% were recorded, respectively. The anticancer activity was also confirmed by phase contrast microscopy, where the treated cells exhibited deformed morphologies. This study demonstrates the potential of the CH-CuO nanoparticle as an effective antibacterial agent, having with its antibiofilm activity, and in cancer treatment.

This study was designed to investigate the occurrence and dissemination of extended-spectrum β-lactamase (ESBL) harboring Klebsiella pneumoniae in various ecological niches under the one health approach. A total of 793 samples were collected from animals, humans, and the environment. The findings of the study revealed the occurrence of K. pneumoniae as follows: animals (11.6%), humans (8.4%), and associated environments (7.0%), respectively. A high occurrence rate of ESBL genes was found in animals compared to human and environmental isolates. A total of 18 distinct sequence types (STs) and 12 clonal complexes of K. pneumoniae were observed. Overall, six STs of K. pneumoniae were identified in commercial chickens, and three were found in rural poultry. The majority of K. pneumoniae STs found in this study were positive for blaSHV, while the positivity of other ESBL-encoding genes combinations was different in different STs. The high occurrence rate of ESBL-harboring K. pneumoniae found in animals as compared to other sources is alarming and has the potential to be disseminated to the associated environment and community.

The transport sector has been hit hard by the COVID-19 pandemic disrupting travel behaviors and mobility patterns around the globe. The pandemic has also affected mode choice behavior. This research study modeled the mode choice behavior before and during the COVID-19 pandemic in Pakistan. Data was collected through an online questionnaire survey consisting of questions about socio-economic characteristics, factors affecting mode choice, and mode chosen for shorter as well as longer distances for both before and during COVID-19 pandemic situations. The results indicated that public transport use declined, whereas walking and bicycling slightly increased during the pandemic. The respondents placed more priority on safety and security, comfort, cleanliness, infection concerns, personal social status, availability of hand-sanitizers, waiting, and paying more for less congested vehicles during the pandemic. Factor analysis was performed to explore the underlying factors affecting mode choice before and during the pandemic. Discrete choice models were developed to model the mode choice behavior. Monthly household income and pandemic-related underlying factor were significant predictors of mode choice for shorter distances (i.e., < 5 km) during the pandemic. Whereas, gender, car ownership and monthly household income were significant predictors of mode choice for longer distances (i.e., > 5 km) during the pandemic. Understanding the modal shift during a pandemic will surely help urban and transport planners to prepare better for effective transport management in the future. Policy implications are also presented to help policymakers in developing policies for post-pandemic mobility needs, particularly in developing countries.

This study presents a novel approach for forecasting the construction cost index (CCI) of building materials in developing countries. Such estimations are challenging due to the need for a longer time, the influence of inflation, and fluctuating project prices in developing countries. This study used three techniques—a modified Artificial Neural Network (ANN), time series, and linear regression—to predict and forecast the local building material CCI in Pakistan. The predicted CCI is based on materials, including bricks, steel, cement, sand, and gravel. In addition, the swish activation function was introduced to increase the accuracy of the associated algorithms. The results suggest that the ANN model has superior prediction results, with the lowest Mean Error (ME), Mean Absolute Error (MAE), and Theil’s U statistic (U-Stat) values of 0.04, 28.3, and 0.62, respectively. The time series and regression models have ME values of 0.22 and 0.3, MAE values of 30.07 and 28.3, and U-Stat values of 0.65 and 0.64, respectively. The proposed models can assist contractors, project managers, and owners through an accurately estimated cost index. Such accurate CCIs help correctly estimate project budgets based on building material prices to mitigate project risks, delays, and failures.

This study aimed to evaluate the anti-arthritic activity of curcumin and meloxicam co-loaded PLGA nanoparticles in adjuvant-induced arthritic rats. PLGA nanoparticles encapsulating curcumin (nCur) and meloxicam (nMlx) alone and in combination (nCur/Mlx) were used to characterize zeta size and potential, polydispersity index, encapsulation efficiency (%), compound–polymer interactions (FT-IR analysis), and surface morphology (SEM imaging). In vivo, Complete Freund’s adjuvant-induced arthritic rats were intraperitoneally (i.p.) administered with curcumin, meloxicam, curcumin plus meloxicam, nCur, nMlx, and nCur/Mlx for 28 consecutive days. Results showed that nCur, nMlx, and nCur/Mlx significantly (p ≤ 0.05) reduced paw swelling and arthritic score, restored body weight and the immune organ index (thymus and spleen), as well as attenuated serum inflammatory markers (RF, CRP, and PGE2) and oxidative stress parameters (MDA, SOD, and CAT) in adjuvant-induced arthritic rats compared to free compounds. In addition, mono- and dual-compound-loaded nanoparticles significantly (p ≤ 0.05) down-regulated pro-inflammatory cytokines (TNF-α, IL-1β, and IL-6), up-regulated anti-inflammatory cytokines (IL-4, IL-10, and IFN-γ), and modulated OPG and RANKL expressions in paw tissue. The aforementioned results were further confirmed through radiological and histopathological examinations. Furthermore, the anti-arthritic effect of nCur/Mlx was notably (p ≤ 0.05) enhanced compared to nCur or nMlx alone. In conclusion, the co-nanoencapsulation of curcumin could potentiate the anti-arthritic activity of meloxicam and could provide a novel therapeutic approach for the formulation of nanocarrier pharmaceutical products for the management of arthritis.

One of the key functions of global water resource management authorities is river water quality (WQ) assessment. A water quality index (WQI) is developed for water assessments considering numerous quality-related variables. WQI assessments typically take a long time and are prone to errors during sub-indices generation. This can be tackled through the latest machine learning (ML) techniques renowned for superior accuracy. In this study, water samples were taken from the wells in the study area (North Pakistan) to develop WQI prediction models. Four standalone algorithms, i.e., random trees (RT), random forest (RF), M5P, and reduced error pruning tree (REPT), were used in this study. In addition, 12 hybrid data-mining algorithms (a combination of standalone, bagging (BA), cross-validation parameter selection (CVPS), and randomizable filtered classification (RFC)) were also used. Using the 10-fold cross-validation technique, the data were separated into two groups (70:30) for algorithm creation. Ten random input permutations were created using Pearson correlation coefficients to identify the best possible combination of datasets for improving the algorithm prediction. The variables with very low correlations performed poorly, whereas hybrid algorithms increased the prediction capability of numerous standalone algorithms. Hybrid RT-Artificial Neural Network (RT-ANN) with RMSE = 2.319, MAE = 2.248, NSE = 0.945, and PBIAS = −0.64 outperformed all other algorithms. Most algorithms overestimated WQI values except for BA-RF, RF, BA-REPT, REPT, RFC-M5P, RFC-REPT, and ANN- Adaptive Network-Based Fuzzy Inference System (ANFIS).

Objective: To explore the protective role of Glinus lotoides ethanolic extract in a depression model through modulating oxidant/antioxidant enzyme system and inflammatory status. Methods: Phytochemical constituents of Glinus lotoides ethanolic extract were evaluated qualitatively and quantitatively along with HPLC. Rats were divided into six groups. The normal control and the intoxicated groups received normal saline, and the standard group received imipramine, while the remaining groups received 100, 300, and 500 mg/kg Glinus lotoides ethanolic extract. All groups received treatments for 14 d. Lipopolysaccharides (LPS) were then administered i.p. (0.83 mg/kg) to all groups except the normal control group. After 24 h, anxiety and depression-like behaviors were evaluated by performing behavioral analysis (open field, tail suspension, forced swim, sucrose preference test), and determining total oxidant status, total antioxidant capacity, catalase, and biochemical parameters [malondialdehyde, glutathione, superoxide dismutase, tumor necrosis factor (TNF)-alpha and interleukin (IL)-6]. Results: Phytochemical studies confirmed the presence of phenols and flavonoids and HPLC analysis showed the presence of gallic acid, quercetin, chlorogenic, and caffeic acid. Total oxidant status was significantly decreased, while total antioxidant capacity was significantly increased in the Glinus lotoides ethanolic extract treated groups. Moreover, Glinus lotoides ethanolic extract diminished malondialdehyde, IL-6, and TNF-alpha levels, while increasing superoxide dismutase, catalase, and glutathione activities. Conclusions: Glinus lotoides ethanolic crude extract shows significant antidepressant activity by modulating oxidative and biochemical parameters that supports its folkloric use in traditional systems of medicine.

The local climate of cities is changing, and one of the primary reasons for this change is rapid urbanization. The Lahore district is situated in the Punjab province of Pakistan and is mainly comprised of Lahore city. This city is among the fastest expanding cities in Pakistan. Due to this rapid urbanization, the natural land surfaces are being altered, harming the local environment and thus causing the urban heat island (UHI) effect. For the analysis of the UHI effect, the fundamental and essential step is assessing the land surface temperature (LST). Therefore, the current investigation assessed LST to evaluate the UHI effect of the Lahore district. This study used the remote sensing data retrieved from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. Different new generation algorithms were initially used, but a convolutional neural network (CNN) model was used based on the accuracy. The model was developed by utilizing the past 19 years’ LST values along with elevation, road density (RD), and enhanced vegetation index (EVI) as input parameters for analyzing and predicting the LST. The LST data of the year 2020 was used for the validation of the outcomes of the CNN model. Among the model predicted LST and observed LST, a high correlation was noticed. The mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) for the considered two different periods (January and May) were also computed for both the training and validation processes. The prediction error for most parts of the district was within 0.1 K of the observed values. Hence, the formulated CNN model can be utilized as an essential tool for analyzing and predicting LST and thus for the evaluation of the UHI effect at any location.

Cycling as an attractive mode of transport is a challenge, especially in developing countries like Pakistan. Previous research on cycling in developing countries is insufficient to answer that how people can be encouraged to bike in different regions and cultures. This research, therefore, directs two research questions based on the perceptions of the people of Lahore. The first research question addresses the perceived motives of everyday biking trip generation and the second question addresses the perceived barriers in biking in the city of Lahore. The data sample of 379 subjects was collected through self-reported questionnaire across different socioeconomic groups. The questionnaire was designed to discuss the motives for biking such as affordability, reliability, and accessibility as well as to identify the barriers such as cultural issues, gender problems and non-availability of infrastructure for biking. Along with descriptive statistics, Multinomial Logistic was used to analyze perceived motives, Binary Logistic for perceived barriers and Ordinal Probit for biking trip generation. The obtained results are very interesting and provide various insights about the perceptions of people regarding biking trip generation, motives, and barriers with various factors involved. The results are beneficial to urban developers, city planners, transport planners, policy makers and other stakeholders.

Abstract ‘Kissing molars’, were described by Van Hoof in 1973 as when the occlusal surfaces of impacted molars are united by the same follicular space and the roots point in the opposite direction. There are very few published cases in the literature, and it is an extremely rare form of impaction.

During the previous few years, the relationship between the gut microbiota, metabolic disorders, and diet has come to light, especially due to the understanding of the mechanisms that particularly link the gut microbiota with obesity in animal models and clinical trials. Research has led to the understanding that the responses of individuals to dietary inputs vary remarkably therefore no single diet can be suggested to every individual. The variations are attributed to differences in the microbiome and host characteristics. In general, it is believed that the immanent nature of host-derived factors makes them difficult to modulate. However, diet can more easily shape the microbiome, potentially influencing human physiology through modulation of digestion, absorption, mucosal immune response, and the availability of bioactive compounds. Thus, diet could be useful to influence the physiology of the host, as well as to ameliorate various disorders. In the present study, we have described recent developments in understanding the disparities of gut microbiota populations between individuals and the primary role of diet-microbiota interactions in modulating human physiology. A deeper understanding of these relationships can be useful for proposing personalized nutrition strategies and nutrition-based therapeutic interventions to improve human health.

Landslides are natural disasters deliberated as the most destructive among the others considered. Using the Muzaffarabad as a case study, this work compares the performance of three conventional Machine Learning (ML) techniques, namely Logistic Regression (LGR), Linear Regression (LR), Support Vector Machine (SVM), and two Multi-Criteria Decision Making (MCDM) techniques, namely Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for the susceptibility mapping of landslides. Most of these techniques have been used in the region of Northern Pakistan before for the same purpose. However, this study for landslide susceptibility assessment compares the performance of various techniques and provides additional insights into the factors used by adopting multicollinearity analysis. Landslide-inducing factors considered in this research are lithology, slope, flow direction, fault lines, aspect, elevation, curvature, earthquakes, plan curvature, precipitation, profile curvature, Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), roads, and waterways. Results show that SVM performs better than LGR and LR among ML models. On the other hand, the performance of AHP was better than TOPSIS. All the models rank slope, precipitation, elevation, lithology, NDWI, and flow direction as the top three most imperative landslide-inducing factors. Results show 80% accuracy in Landslide Susceptibility Maps (LSMs) from ML techniques. The accuracy of the produced map from the AHP model is 80%, but for TOPSIS, it is less (78%). In disaster planning, the produced LSMs can significantly help the decision-makers, town planners, and local management take necessary measures to decrease the loss of life and assets.

Klebsiella pneumoniae is ubiquitous and known to be a notorious pathogen of humans, animals, and plant-based foods. K. pneumoniae is a recognized trafficker of antibiotic resistance genes (ARGs) between and from different ecological niches. A total of 775 samples (n = 775) were collected from September 2017 to August 2019 from humans, animals, and environmental sources by applying the random convenient sampling technique. A total of 120 (15.7%) samples were confirmed as K. pneumoniae. The distribution of K. pneumoniae among humans, the environment, and animals was 17.1, 12.38, and 10%, respectively. Isolates have shown significant resistance against all the subjected antibiotics agents except colistin. ARGs profiling revealed that the highest percentage prevalence (67.5%) of blaCTX–M was estimated in the isolates, and various carbapenem resistance genes that were found in the study were blaNDM–1 (43.3%), blaOXA–48 (38%), and (1.67%) blaKPC–2. Overall, 21 distinct sequence types (ST) and 13 clonal complexes (CCs) were found through the multi-locus sequence typing (MLST) analysis. Taking together, the distribution of multi-drug resistance (MDR) K. pneumoniae clones in the community and associated environment is alarming for the health care system of the country. Health policymakers should consider the role of all the integral parts of humans, animals, and the associated environment intently to cope with this serious public and animal health concern.

Bone tissue engineering is a promising treatment for bone loss that requires a combination of porous scaffold and osteogenic cells. The aim of this study was to evaluate and develop a tricomposite, biomimetic scaffold consisting of marine-derived biomaterials, namely, chitosan and fucoidan with hydroxyapatite (HA). The effects of chitosan, fucoidan and HA individually and in combination on the proliferation and differentiation of human mesenchymal stem cells (MSCs) were investigated. According to the SEM results, the tricomposite scaffold had a uniform porous structure, which is a key requirement for cell migration, proliferation and vascularisation. The presence of HA and fucoidan in the chitosan tricomposite scaffold was confirmed using FTIR, which showed a slight decrease in porosity and an increase in the density of the tricomposite scaffold compared to other formulations. Fucoidan was found to inhibit cell proliferation at higher concentrations and at earlier time points when applied as a single treatment, but this effect was lost at later time points. Similar results were observed with HA alone. However, both HA and fucoidan increased MSC mineralisation as measured by calcium deposition. Differentiation was significantly enhanced in MSCs cultured on the tricomposite, with increased alkaline phosphatase activity on days 17 and 25. In conclusion, the tricomposite is biocompatible, promotes osteogenesis, and has the structural and compositional properties required of a scaffold for bone tissue engineering. This biomaterial could provide an effective treatment for small bone defects as an alternative to autografts or be the basis for cell attachment and differentiation in ex vivo bone tissue engineering.

Herein, we evaluated the in vivo effects of meloxicam and curcumin co-encapsulated PLGA nanoparticles in experimental acute models of pyrexia, nociception, and inflammation. Seven groups (n = 6) were designed for each investigation and pretreated intraperitoneally (i.p.): the control group, meloxicam (4 mg/kg b.w.), curcumin (15 mg/kg b.w.), and equivalent content containing PLGA capped nanoparticles of meloxicam (Mlx-NP) and curcumin (Cur-NP) alone and in combination (Mlx-Cur-NP; at two doses). The results showed that PLGA encapsulation significantly (p ≤ 0.05) improved the in vivo activities of each compound. Furthermore, co-encapsulation of meloxicam and curcumin potentiated the anti-pyretic effect on yeast-induced pyretic rats, anti-nociceptive effect on nociception induced in rats by formalin and heat, and anti-edematogenic activity in xylene-induced ear edema in rats in a dose-dependent manner. In carrageenan-induced paw inflammation in rats, meloxicam and curcumin co-loading (Mlx-Cur-NP) resulted in significant (p ≤ 0.05) inhibition of paw inflammation, reduction in TNF-α and PGE2 levels, downregulation of expressions of pro-inflammatory cytokines (TNF-α, IL-1β, and IL-6), as well as a decrease in histopathological changes and TNF-α immunoexpression in paw tissues. Moreover, Mlx-Cur-NP demonstrated noteworthy potentiation in pharmacological effects compared to free compounds and mono-compound-loaded nanoparticles. Thus, the association of meloxicam with curcumin in a biodegradable nanocarrier system could provide a promising anti-pyretic, anti-nociceptive, and anti-inflammatory therapeutic approach for acute conditions.

Acinetobacter baumannii, a pathogenic bacterium acquired in hospitals, causes diverse infections in humans. Previous studies have reported resistance among A. baumannii strains, potentially selecting multi-drug-resistant variants. In Pakistan, research has primarily focused on carbapenem-resistant A. baumannii (CRAB) strains, overlooking the investigation of efflux pumps (EPs) and biocide resistance. This study aims to assess A. baumannii strains from five hospitals in Pakistan, focusing on antibiotic and biocide susceptibility, the impact of EP inhibitors on antimicrobial susceptibility, and the distribution of ARGs and STs. A total of 130 non-repeated Acinetobacter baumannii isolates were collected from five tertiary care hospitals in Pakistan and identified using API 20NE and multiplex PCR. Antimicrobial susceptibility testing utilized disc diffusion and broth microdilution assays, while biocide susceptibility was assessed with various agents. The impact of an efflux pump inhibitor (NMP) on antibiotic susceptibility was evaluated. PCR screening for ARGs and EPGs was followed by DNA sequencing validation. MLST was performed using the Pasteur scheme. Most isolates demonstrated resistance to tested antibiotics, with varying levels of susceptibility to biocides. All isolates exhibited the intrinsic class D β-lactamase blaOXA-51, while acquired blaOXA-23 was present in all CRAB isolates. Among EPs, adeJ, abeD, amvA, and aceI were prevalent in almost all isolates, with adeB found in 93% of isolates and adeG, adeT1, adeT2, and qacEΔ1 displaying lower prevalence ranging from 65% to 79%. The most common STs were ST589 and ST2, accounting for 28.46% and 25.38% of isolates, respectively, followed by ST642 at 12.6%. These findings indicate that A. baumannii strains in Pakistan are resistant to antibiotics (excluding colistin and tigecycline) and may be developing biocide resistance, which could contribute to the selection and dissemination of multi-drug-resistant strains.

Objective(s): This study aimed to evaluate the anti-arthritic activity of Ricinus communis leaves’ and Withania somnifera roots’ hydroalcoholic extracts in Complete Freund’s adjuvant-induced arthritis in Wistar rats. Materials and Methods: HPLC and FT-IR analysis detected pharmacologically important phytocompounds in both plant extracts. Oral treatments including methotrexate (MTX; 3 mg/kg twice a week) and extracts at 250 and 500 mg/kg/day were initiated after arthritis induction. Changes in paw swelling, arthritic score, body weight, organ indices (thymus and spleen), hematological and biochemical parameters, and pro-/anti-inflammatory cytokine expression using qRT-PCR were assessed. Oxidative stress markers in hepatic tissue were determined. Histopathological and radiological examinations were also performed. Results: RCE (R. communis extract) and WSE (W. somnifera extract) demonstrated a reduction in paw swelling, arthritic score, and restoration of body weight and organ indices. Hematological parameters, serum inflammatory markers such as CRP and RF, and liver function markers of arthritic rats were significantly (p

Soil erosion has serious threats to agricultural production, hydraulic structures, and the world’s ecosystem. The objective of this study is the delineation of soil erosion susceptibility zones in the Chitral district using spatial analyst tool in conjunction with Analytical Hierarchy Process (AHP). This district is highly vulnerable to soil erosion due to the mountainous topography. For the sustainability of agricultural land use as well as regional and local development, it is necessary to find out soil erosion probability zones and soil loss at the watershed scale. Eleven different factors; lithology, slope, elevation, plain curvature, lineaments, land cover, aspect, rainfall, drainage density, NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) are considered in this study. Weights have been assigned to each factor, and maps have been generated through GIS (Geographic Information System) tools. The final map from the combination of all maps shows intensities of soil erosion in five different classes including very high, high, medium, low and very low. Very high and high erosion is observed in 13% and 18% of the total study area respectively, which shows that area is under serious danger of soil erosion. Elevation, slope, curvature, NDWI, and rainfall are found to be the dominant factors influencing soil erosion process. This study highlighted areas at risk of severe erosion which will be helpful for researchers and planners to plan for control of soil erosion.

The development of bacterial resistance toward existing antibiotics is a universal problem for human and animal health as well as for food security. In an attempt to overcome this problem, nanotechnology has contributed in the form of nanoformulations. However, these are associated with risks and drawbacks including environmental toxicity, cell injury, issues of high production cost and the scarcity of active ingredients. On the other hand, the green synthesis of nanoformulations via biological methods is a simple, innovative, ecofriendly, cost effective and advanced approach for the treatment of lethal infections caused by multidrug-resistant organisms such as staphylococcus aureus. About 30% of humans are asymptomatic carriers of S. aureus in their upper respiratory tract. Clinical diseases caused by S. aureus infections range from mild to severe and may be manifested in the form of pneumonia, osteomyelitis, skin and deep tissue infections. Here, we prepared plant-mediated gold nanoparticles from Camellia sinensis and Cocos nucifera. The green biocompatible nanoparticles were characterized by using UV-Visible spectroscopy (UV-Vis. spectroscopy), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), dynamic light scattering (DLS) and Fourier transform infrared spectroscopy (FTIR). Moreover, these green gold nanoparticles were investigated for their antimicrobial activity by checking their minimum inhibitory concentrations (MICs). We found that the newly developed bio-nanoparticles showed strong activity against the multidrug-resistant Staphylococcus aureus.

Avian pathogenic Escherichia coli (APEC) is responsible for significant economic losses in the poultry industry. This study aimed to molecularly detect carbapenem-resistant co-harboring mcr-1 avian pathogenic E. coli in broiler chickens infected with colibacillosis. A total of 750 samples were collected from colibacillosis-infected broilers, and conventional microbiological techniques were used to isolate and identify APEC. MALDI-TOF and virulence-associated genes (VAGs) were used for further identification. Phenotypic carbapenem resistance profiling was followed by molecular detection of carbapenem resistance genes (CRGs) and other resistance genes through PCR using specific primers. Isolates were also subjected to PCR for O typing, followed by allele-specific PCR to detect sequence type (ST) 95. Results showed that 154 (37%) isolates were confirmed as APEC, with 13 (8.4%) isolates found to be carbapenem-resistant (CR)-APEC. Among CR-APEC isolates, 5 (38%) were observed to co-harbor mcr-1. All CR-APEC showed the presence of five markers (ompT, hylF, iutA, iroN, and iss) APEC VAGs, and 89% of CR-APEC isolates displayed O78 type. Furthermore, 7 (54%) CR-APEC isolates were observed with ST95, all displaying O78 type. These results suggest that the improper use of antibiotics in poultry production systems is contributing to the emergence of pathogens such as CR-APEC co-harboring the mcr-1 gene.

As is well known, plant products have been increasingly utilized in the pharmaceutical industry in recent years. By combining conventional techniques and modern methodology, the future of phytomedicines appears promising. Pogostemon Cablin (patchouli) is an important herb used frequently in the fragrance industries and has various therapeutic benefits. Traditional medicine has long used the essential oil of patchouli (P. cablin) as a flavoring agent recognized by the FDA. This is a gold mine for battling pathogens in China and India. In recent years, this plant has seen a significant surge in use, and approximately 90% of the world’s patchouli oil is produced by Indonesia. In traditional therapies, it is used for the treatment of colds, fever, vomiting, headaches, and stomachaches. Patchouli oil is used in curing many diseases and in aromatherapy to treat depression and stress, soothe nerves, regulate appetite, and enhance sexual attraction. More than 140 substances, including alcohols, terpenoids, flavonoids, organic acids, phytosterols, lignins, aldehydes, alkaloids, and glycosides, have been identified in P. cablin. Pachypodol (C18H16O7) is an important bioactive compound found in P. cablin. Pachypodol (C18H16O7) and many other biologically essential chemicals have been separated from the leaves of P. cablin and many other medicinally significant plants using repeated column chromatography on silica gel. Pachypodol’s bioactive potential has been shown by a variety of assays and methodologies. It has been found to have a number of biological activities, including anti-inflammatory, antioxidant, anti-mutagenic, antimicrobial, antidepressant, anticancer, antiemetic, antiviral, and cytotoxic ones. The current study, which is based on the currently available scientific literature, intends to close the knowledge gap regarding the pharmacological effects of patchouli essential oil and pachypodol, a key bioactive molecule found in this plant.

The determinants of biking behaviour are less studied in a wide range of developing countries including South Asia. This study takes Lahore, Pakistan as a case-study city to explore the factors defining commute and non-commute bike trips as well as commuting by bike. These issues were analysed by collecting data from 379 subjects accommodating in three socio-economic statuses (lower, medium, and higher) in Lahore in spring 2018. The data were analysed by applying multinomial logistic regression for investigating biking frequency and binomial logistic regression for examining commuting by bike. The results show that gender, age, education, income, purpose of majority of trips, preferred distance to travel using cycle, preferred time to travel using cycle, and preferred bike trip purpose are significantly correlated with biking frequency. The significant determinants of bicycle commuting included categories of education, the purpose of the majority of trips, using bike in combination with other modes, preferred distance to bike, preferred biking time, and preferred bike trip purpose are associated with bicycle commuting. Commuting by bike is more popular in socio-economically weaker neighbourhoods. The discussion of this study shows that the determinants of biking in the sample in Lahore are different from those that have already been addressed by studies undertaken in high-income countries.

This study was designed to determine the in vitro antidiabetics and antioxidant ability of Abelmoschus esculentus methanolic extract (MEAE) using α-amylase and α-glucosidase inhibition model. α-amylase and α-glucosidase binding interaction with MEAE was also determined by molecular docking. Methanolic extract of A. esculentus was prepared and then total phenolic content (TPC) and total flavonoid content (TFC) was determined. TPC and TFC was 153 ± 38.82 mg GAE/g dw and 40.55 ± 4.409 mg QE/g dw respectively. For antioxidant potential, DPPH method was used. At concentrations of 31–1000 µg/mL MEAE showed scavenging percentage of DPPH radicals ranging from 1.618 to 13.487%. Enzymatic antioxidant activities by MEAE such as catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD) showed their significant potentials with values of 3.99 ± 1.05, 1.27 ± 2.6 and 23.80 ± 0.03 U/mg protein respectively. At 50–200 µg/mL inhibition percentage of α-glucosidase and α-amylase ranged from 14.36 ± 0.099 to 19.23 ± 0.172% and 15.89 ± 1.877 to 37.19 ± 7.430% respectively. Yeast glucose uptake assay revealed that it showed glucose absorption which was directly proportional with concentration of extract and inversely proportional with the molar concentration of glucose. Maximum glucose uptake percentage by MEAE was 68.420 ± 1.752% at 3 mg/mL extract and 5 mM glucose concentration. In silico analysis, revealed that ligand molecule could bind with the α-glucosidase and α-amylase molecules. It can be concluded that A. esculentus is an important vegetable with a potent source of natural antioxidants as well as having potential antidiabetic activity thus justifying its traditional use in green therapeutics.

Objectives: The rise of carbapenem resistance in Acinetobacter baumannii represents a challenge for the therapeutic management of infections. The present study aimed to investigate the sequence types (STs) and carbapenem resistance in A. baumannii strains collected from various clinical specimens from patients admitted to five tertiary-care hospitals in Pakistan. Methods: A total of 156 A. baumannii clinical strains were analysed for antimicrobial susceptibility, followed by genetic screening for carbapenem resistance determinants. All of the strains were typed by multilocus sequence typing (MLST) according to the Pasteur scheme. Results: Of the 156 A. baumannii isolates, 139 (89.1%) were carbapenem-resistant, of which 136 carried blaOXA-23-like genes. Interestingly, the most commonly identified ST was ST589 (n = 52), classified as clonal complex 1 (CC1). ST2 was the second most common (n = 38), corresponding to CC2/92 (Pasteur/Oxford scheme), which was distributed in all five hospitals. Conclusion: Diverse clones of carbapenem-resistant A. baumannii, including previously reported STs as well as new STs, carrying blaOXA-23 are distributed in Pakistan. This is the first study to describe the molecular epidemiology of widely disseminated A. baumannii isolates in Pakistan. The findings will help to improve our knowledge of the predominant STs and will be valuable for a deeper understanding of resistance mechanisms among various STs.

The emergence and re-emergence of zoonotic diseases significantly impact human health, particularly those who live in impoverished areas and have close contact with domestic or wild animals. Nearly 75% of zoonotic diseases are transmitted directly from animals to humans or indirectly via vector/agent interactions between animals and humans. Growing populations, globalization, urbanization, and the interaction of the environment with humans and livestock all play roles in the emergence and spread of zoonotic diseases. “One Health” is a multidisciplinary concept aimed at improving human, animal, and environmental health, but this concept is not widely accepted in developing countries. In Pakistan, environmental, human, and animal health are severely affected due to a lack of sufficient resources. This review article provides an overview of the most common zoonotic diseases found in Pakistan and emphasizes the importance of the “One Health” concept in managing these diseases. Given the current situation, interdisciplinary research efforts are required to implement and sustain effective and long-term control measures in animal, human, and environmental health surveillance and accurate diagnostic methods.

Emergence of multidrug resistant pathogens is increasing globally at an alarming rate with a need to discover novel and effective methods to cope infections due to these pathogens. Green nanoparticles have gained attention to be used as efficient therapeutic agents because of their safety and reliability. In the present study, we prepared zinc oxide nanoparticles (ZnO NPs) from aqueous leaf extract of Acacia arabica. The nanoparticles produced were characterized through UV-Visible spectroscopy, scanning electron microscopy, and X-ray diffraction. In vitro antibacterial susceptibility testing against foodborne pathogens was done by agar well diffusion, growth kinetics and broth microdilution assays. Effect of ZnO NPs on biofilm formation (both qualitatively and quantitatively) and exopolysaccharide (EPS) production was also determined. Antioxidant potential of green synthesized nanoparticles was detected by DPPH radical scavenging assay. The cytotoxicity studies of nanoparticles were also performed against HeLa cell lines. The results revealed that diameter of zones of inhibition against foodborne pathogens was found to be 16–30 nm, whereas the values of MIC and MBC ranged between 31.25–62.5 μg/ml. Growth kinetics revealed nanoparticles bactericidal potential after 3 hours incubation at 2 × MIC for E. coli while for S. aureus and S. enterica reached after 2 hours of incubation at 2 × MIC, 4 × MIC, and 8 × MIC. 32.5–71.0% inhibition was observed for biofilm formation. Almost 50.6–65.1% (wet weight) and 44.6–57.8% (dry weight) of EPS production was decreased after treatment with sub-inhibitory concentrations of nanoparticles. Radical scavenging potential of nanoparticles increased in a dose dependent manner and value ranged from 19.25 to 73.15%. Whereas cytotoxicity studies revealed non-toxic nature of nanoparticles at the concentrations tested. The present study suggests that green synthesized ZnO NPs can substitute chemical drugs against antibiotic resistant foodborne pathogens.

Abstract Ricinus communis L. and Withania somnifera L. have traditionally been used as analgesic and anti-inflammatory remedies. The current study was aimed to evaluate the in vitro antioxidant potential and anti-inflammatory activity of hydroalcoholic extract of R. communis leaves (RCE) and W. somnifera roots (WSE) in Wistar rats. Total phenolic and flavonoid contents were quantified and in vitro antioxidant activity of extracts was determined through DPPH* scavenging, superoxide anion scavenging and reducing power activities, while anti-inflammatory activity was observed by xylene-induced ear edema and paw edema induced by egg albumin and carrageenan. RCE and WSE demonstrated considerable antioxidant activity in DPPH* scavenging (IC50: 250.10 and 309.42 µg/mL), superoxide anion scavenging (IC50: 193.42 and 206.81 ug/mL), and reducing power (maximum absorbance: 1.47±0.01 O.D and 1.28±0.01 O.D at 500 ug/mL) activities, respectively, with high phenolic and flavonoid contents. Both extracts showed dose-dependent edema inhibition in inflammation models. A maximum ear edema inhibitions by RCE (51.49±2.54%) and WSE (49.28±1.90%) at 500 mg/kg were observed when compared to indomethacin (56.42±13.17%) in xylene-induced ear edema. RCE and WSE showed a maximum percentage of paw edema inhibitions of 46.62±8.98% and 43.00±12.44%, respectively as compared to chlorpheniramine (62.02±12.21%) after 4 h in the egg albumin model. In carrageenan-induced paw edema, RCE (72.88±13.79%) significantly inhibited paw edema in comparison to WSE (57.81±17.43%) against diclofenac (89.93±18.53%). Conclusively, both plants have shown plausible antioxidant and anti-inflammatory activities that might be due to high phenolic and flavonoid contents. Moreover, RCE demonstrated more promising effects than WSE.

Antibiotic resistance (ABR) is a growing public health concern worldwide, and it is now regarded as a critical One Health issue. One Health’s interconnected domains contribute to the emergence, evolution, and spread of antibiotic-resistant microorganisms on a local and global scale, which is a significant risk factor for global health. The persistence and spread of resistant microbial species, and the association of determinants at the human-animal-environment interface can alter microbial genomes, resulting in resistant superbugs in various niches. ABR is motivated by a well-established link between three domains: human, animal, and environmental health. As a result, addressing ABR through the One Health approach makes sense. Several countries have implemented national action plans based on the One Health approach to combat antibiotic-resistant microbes, following the Tripartite’s Commitment Food and Agriculture Organization (FAO)-World Organization for Animal Health (OIE)-World Health Organization (WHO) guidelines. The ABR has been identified as a global health concern, and efforts are being made to mitigate this global health threat. To summarize, global interdisciplinary and unified approaches based on One Health principles are required to limit the ABR dissemination cycle, raise awareness and education about antibiotic use, and promote policy, advocacy, and antimicrobial stewardship.

Control of the cattle tick Rhipicephalus (R.) microplus mainly relies on chemical acaricides and cypermethrin is the most widely used acaricide in Pakistan. Farmers frequently complain about its low efficacy, thus, the present study was designed to quantify the frequency of cypermethrin resistance in cattle ticks. Engorged female R. microplus were collected and tested for the efficacy of cypermethrin using the FAO-recommended larval packet test. Resistance factors (RF) were estimated at both the lethal concentration for 50% (LC50) and 99% (LC99) of ticks. Thirty-three samples were tested, of which 8/33 (24.24%) were classified as resistant based on the RF50, and all 33 were classified as resistant based on the RF99. In District Sargodha, when only the RF50 was considered, 45.5% of samples were classified as resistant, but at RF99, all tested samples were identified as resistant. In District Okara, the variation in RF50 estimates was 2.2–8.3 and variation in RF99 estimates was 10.6–1139.8. Similar results were found in District Attock, where variations in RF50 were 0.8–8.5 and RF99 ranged from 9–237.3. The study showed that cypermethrin resistance is prevalent in these three districts of Pakistan and is likely to be overestimated by classification based on the RF99.

Northern Pakistan, the center of major construction projects due to the commencement of the China Pakistan Economic Corridor, is among the most earthquake-prone regions globally owing to its tectonic settings. The area has experienced several devastating earthquakes in the past, and these earthquakes pose a severe threat to infrastructure and life. Several researchers have previously utilized advanced tools such as Machine Learning (ML) and Deep Learning (DL) algorithms for earthquake predictions. This technological advancement helps with construction innovation, for instance, by designing earthquake-proof buildings. However, previous studies have focused mainly on temporal rather than spatial variables. The present study examines the impact of spatial variables to assess the performance of the different ML and DL algorithms for predicting the magnitude of short-term future earthquakes in North Pakistan. Two ML methods, namely Modular Neural Network (MNN) and Shallow Neural Network (SNN), and two DL methods, namely Recurrent Neural Network (RNN) and Deep Neural Network (DNN) algorithms, were used to meet the research objectives. The performance of the techniques was assessed using statistical measures, including accuracy, information gain analysis, sensitivity, specificity, and positive and negative predictive values. These metrics were used to evaluate the impact of including a new variable, Fault Density (FD), and the standard seismic variables in the predictions. The performance of the proposed models was examined for different patterns of variables and different classes of earthquakes. The accuracy of the models for the training data ranged from 73% to 89%, and the accuracy for the testing data ranged from 64% to 85%. The analysis outcomes demonstrated an improved performance when using an additional variable of FD for the earthquakes of low and high magnitudes, whereas the performance was less for moderate-magnitude earthquakes. DNN, and SNN models, performed relatively better than other models. The results provide valuable insights about the influence of the spatial variable. The outcome of the present study adds to the existing pool of knowledge about earthquake prediction, fostering a safer and more secure regional development plan involving innovative construction.