Poultry consumption and perceptions in Tehsil Shakargarh, Punjab, Pakistan: Implications for public health during COVID-19.

This study investigated the habits and attitudes of individuals towards poultry consumption, utilizing primary data collected through a survey of 5 households from 285 localities in Tehsil Shakargarh, Punjab, Pakistan (n = 1425). Household selection was randomized, and personal visits were conducted for data collection via formal interviews employing a structured questionnaire. Coordinates for each site were obtained using a Garmin eTrex device, in conjunction with meteorological data, to determine global positioning system (GPS) coordinates. A notable portion of respondents (38.8 %) possessed basic knowledge, while the majority (61.2 %) demonstrated intermediate knowledge regarding commercial broilers (chickens raised for meat production). A significant proportion (70.3 %) harbored misconceptions about the inclusion of hormones/antibiotics in poultry feed, with a minority (0.2 %) misinformed about broiler chickens' leg weakness. Some respondents (17.3 %) held both misconceptions, while others (12.2 %) had none. The majority (97.6 %) favored egg consumption, with 51.7 % preferring commercial chicken eggs and 48.3 % opting for domestic chicken eggs. Preference for white-colored eggs (51.5 %) slightly outweighed that for brown-colored eggs (48.5 %). A minority (1.3 %) speculated that poultry consumption could be a potential cause of COVID-19, while the majority (65.7 %) disagreed, and a portion (33.0 %) remained uncertain. Nearly all respondents (99.9 %) believed in the immunity-boosting properties of protein intake, with 65 % associating such benefits with poultry meat and eggs. Similarly, 99.7 % did not encounter difficulties in accessing poultry products during lockdowns. Approximately half (46.3 %) of respondents believed that consuming well-cooked and safely handled poultry meat was safe during outbreaks. Poultry meat and eggs emerged as potentially efficient sources of nutrition during the COVID-19 pandemic, especially for protein-deficient populations like Pakistan. Therefore, initiatives should focus on enhancing commercial poultry production and educating the populace about its advantages.

Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning.

COVID-19 has infected millions of people worldwide over the past few years. The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise. X-ray imaging is an alternative and more accessible technique. This study aimed to improve detection accuracy to create a computer-aided diagnostic tool. Combining other artificial intelligence applications techniques with radiological imaging can help detect different diseases. This study proposes a technique for the automatic detection of COVID-19 and other chest-related diseases using digital chest X-ray images of suspected patients by applying transfer learning (TL) algorithms. For this purpose, two balanced datasets, Dataset-1 and Dataset-2, were created by combining four public databases and collecting images from recently published articles. Dataset-1 consisted of 6000 chest X-ray images with 1500 for each class. Dataset-2 consisted of 7200 images with 1200 for each class. To train and test the model, TL with nine pretrained convolutional neural networks (CNNs) was used with augmentation as a preprocessing method. The network was trained to classify using five classifiers: two-class classifier (normal and COVID-19); three-class classifier (normal, COVID-19, and viral pneumonia), four-class classifier (normal, viral pneumonia, COVID-19, and tuberculosis (Tb)), five-class classifier (normal, bacterial pneumonia, COVID-19, Tb, and pneumothorax), and six-class classifier (normal, bacterial pneumonia, COVID-19, viral pneumonia, Tb, and pneumothorax). For two, three, four, five, and six classes, our model achieved a maximum accuracy of 99.83, 98.11, 97.00, 94.66, and 87.29%, respectively.

Pathological-Gait Recognition Using Spatiotemporal Graph Convolutional Networks and Attention Model.

Walking is an exercise that uses muscles and joints of the human body and is essential for understanding body condition. Analyzing body movements through gait has been studied and applied in human identification, sports science, and medicine. This study investigated a spatiotemporal graph convolutional network model (ST-GCN), using attention techniques applied to pathological-gait classification from the collected skeletal information. The focus of this study was twofold. The first objective was extracting spatiotemporal features from skeletal information presented by joint connections and applying these features to graph convolutional neural networks. The second objective was developing an attention mechanism for spatiotemporal graph convolutional neural networks, to focus on important joints in the current gait. This model establishes a pathological-gait-classification system for diagnosing sarcopenia. Experiments on three datasets, namely NTU RGB+D, pathological gait of GIST, and multimodal-gait symmetry (MMGS), validate that the proposed model outperforms existing models in gait classification.

All optical control of magnetization in quantum confined ultrathin magnetic metals.

All-optical control dynamics of magnetization in sub-10 nm metallic thin films are investigated, as these films with quantum confinement undergo unique interactions with femtosecond laser pulses. Our theoretical analysis based on the free electron model shows that the density of states at Fermi level (DOSF) and electron-phonon coupling coefficients (Gep) in ultrathin metals have very high sensitivity to film thickness within a few angstroms. We show that completely different magnetization dynamics characteristics emerge if DOSF and Gep depend on thickness compared with bulk metals. Our model suggests highly efficient energy transfer from femtosecond laser photons to spin waves due to minimal energy absorption by phonons. This sensitivity to the thickness and efficient energy transfer offers an opportunity to obtain ultrafast on-chip magnetization dynamics.

Seroprevalence and risk factors of avian influenza H9 virus among poultry professionals in Rawalpindi, Pakistan.

BACKGROUND: Avian influenza H9 is endemic in commercial and backyard poultry in Pakistan and is a serious occupational health hazard to industry workers. This study aimed to determine the seroprevalence of avian influenza H9 infection in people working with poultry in Rawalpindi, Pakistan and assess the measures they took to protect themselves from infection. METHODS: A cross-sectional study was conducted from December 2016 to May 2017 of 419 people working with poultry in Rawalpindi Division, including farm workers, vaccinators, field veterinarians, butchers and staff working in diagnostic laboratories. Potential participants were randomly approached and gave written consent to participate. Data were collected using a standardized questionnaire and serum samples were processed to detect H9 antibodies using the haemagglutination inhibition test. RESULTS: Of the 419 participants, 406 (96.9%) were male. The mean age of the participants was 36.4 (SD 10.86) years. A total of 332 participants agreed to a blood test, 167 of whom were positive for A(H9) antibodies, giving an overall seroprevalence of 50.3%. Laboratory staff had the highest seroprevalence (100%) and veterinarians the lowest (38.5%). Vaccinators, butchers and farm workers had a seroprevalence of 83.3%, 52.4% and 45.5% respectively. Personals who used facemasks had significantly lower (P<0.002) seroprevalence (29.6%) than those who never used them (90.6%). Similarly, those who always used gloves and washed their hands with soap had a seroprevalence of 32.8% compared with 89.0% in those who never took these precautions. Of the participants who handled antigens, 92.3% were seropositive. CONCLUSION: Laboratory staff and vaccinators are exposed to viral cultures and influenza vaccines respectively which may explain their high seroprevalence.