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IntroductionNorovirus, mainly associated with acute gastroenteritis, is very contagious and can affect a vast range of species ranging from cattle, pigs, dogs, mice, cats, sheep, and lions to humans. It is a foodborne pathogen that mainly transmits through the fecal–oral route.MethodsThis is the first-ever study conducted in Lahore and Sheikhupura districts of Punjab, Pakistan, to investigate noroviruses through the One Health approach. From January 2020 to September 2021, 200 fecal samples were collected from clinical cases of hospitalized patients and 200 fecal samples from sick animals at veterinary hospitals and local farms. In addition, 500 food and beverage samples were collected from street vendors and retail stores. A predesigned questionnaire was used to assess the risk factors and clinical characteristics of sick people and animals.Results and discussionOverall, 14% of the human clinical samples were positive by RT-PCR for genogroup GII. All bovine samples were negative. Food and beverage samples were tested in pools, resulting in sugarcane juice samples positive for genogroup GII. Previous contact with acute gastroenteritis patients, sex, and presence of vomiting were found to be significant risk factors (p ≤ 0.05). The substantial number of diarrhea cases associated with noroviruses calls for additional studies to investigate the epidemiology and transmission and to improve surveillance.

Abstract Background Cutaneous Leishmaniasis (CL) is a neglected tropical disease, which mainly affects poor communities. It is one of the major vector-borne disease and endemic in Pakistan. Methods A case-control study to evaluate potential risk factors of human-CL was conducted in Khewra region, District Jhelum, Pakistan from January–April 2014. Case data about 90 cases registered during October 2012 to November 2013 was retrieved from Municipal Hospital. Controls were matched (1,1 ratio) on the date of registration with cases from same hospital. Both cases and controls were invited to participate and data was collected in a face-to-face interview. A prospective study of canine leishmaniasis (canine-CL) was also conducted at Civil Veterinary Hospital in the same area. Suspected dogs with skin ulceration signs were included in the study and blood samples were collected. Statistical analyses were conducted to determine association between various parameters and outcome of interest. Results The ages of cases ranged from 1 to 76 years (median = 15 years) and proved to be protective factor i.e. increase in each year in age reduced the likelihood of being infected with human-CL [Odds Ratio (OR) = 0.4, 95% Confidence Interval (CI) = 0.25–0.76]. People sleeping outsides in an open area were more likely to become a case (OR = 8.7, 95% CI = 2.90–26.37) than a control. Poor sanitary condition inside the house (OR = 3.3, 95% CI 1.03–10.56) and presence of other animals in house (livestock, poultry) (OR = 3.6, 95% CI = 1.07–12.12) also identified as risk factors of high significance. The proportion of positive dogs with canine-CL was 21.05% and was significantly associated with human-CL cases in the same area (p

Abstract Background This study aimed to assess the extent of knowledge and understanding of rabies disease in rural and urban communities of Pakistan. It also identified malpractices after suspected dog bite that might pose a risk for humans contracting rabies. Methods A cross-sectional study was conducted (n = 1466) on people having different age groups and educational levels in four different geographic regions of Punjab and Khyber Pakhtunkhwa provinces in Pakistan. Knowledge, attitude, and practices of people were assessed using a structured questionnaire. We used a bivariate and multivariate analysis to study the association between rabies related mortalities in near or extended family members and different risk behaviors. Results Our results demonstrate that the majority of the juvenile population (less than 18 years of age) were not aware of the clinical signs of rabies in animals. 75% of the total respondents were not vaccinated against rabies, 60% did not seek a doctor’s advice after a suspected animal bite, and 55% had inadequate health care facilities for rabies patients in local hospitals. Respondents that had pets at home had not vaccinated (38%; p

Studies on the impact of the COVID-19 pandemic on animal welfare and dairy husbandry in low-income countries are limited. We conducted a survey between February and June 2020 to evaluate the economic impact and animal health problems upon the pandemic. Participants were lead veterinarians from 14 dairy farms with herd size between 100 and 500 in Lahore. These farms were major suppliers of milk and dairy products to central Punjab, Pakistan. During the pandemic, 10 of the 14 dairy farms relied on feed mill concentrates to provide feeds to their herds. Half of the farms reported feed shortage due to lockdowns. Six (43%) dairy farms have witnessed a 7.5% shortage of dry feed intake. In seven (50%) farms, the body condition score decreased by 0.24 point. The body score reduction was significantly associated with depleted feed intake (P = 0.005). The veterinarians of 10 (71%) farms failed to gain access to essential veterinary medications, hampering the treatment of sick animals. Due to feed shortage and drug unavailability, daily milk production reduced by two litters per cow in the herd of five (35%) farms. The reduced feed intake was significantly associated with the decrease in milk production (P = 0.003), while numerous downstream milk-processing facilities were out of service during the pandemic, significantly reducing the profit of six (43%) dairy farms. Finally, our study showed that the dairy farming industry and animal welfare were critically affected by three aspects: feed shortage, inaccessibility to essential veterinary drugs, and a reduced consumer demand for dairy products.

Choosing an optimal artificial intelligence (AI) provider involves multiple factors, including scalability, cost, performance, and dependability. To ensure that decisions align with organizational objectives, multi-attribute decision-making (MADM) approaches aid in the systematic evaluation and comparison of AI vendors. Therefore, in this article, we propose a MADM technique based on the framework of the complex intuitionistic fuzzy rough model. This approach effectively manages the complex truth grade and complex false grade along with lower and upper approximation. Furthermore, we introduced aggregation operators based on Dombi t-norm and t-conorm, including complex intuitionistic fuzzy rough (CIFR) Dombi weighted averaging (CIFRDWA), CIFR Dombi ordered weighted averaging (CIFRDOWA), CIFR Dombi weighted geometric (CIFRDWG), and CIFR Dombi ordered weighted geometric (CIFRDOWG) operators, which were integrated into our MADM technique. We then demonstrated the application of this technique in a case study on AI provider selection. To highlight its advantages, we compared our proposed method with other approaches, showing its superiority in handling complex decision-making scenarios.