Recent developments in targeting breast cancer stem cells (BCSCs): a descriptive review of therapeutic strategies and emerging therapies.

Despite recent advancements in the diagnosis and treatment of breast cancer (BC), patient outcomes in terms of survival, recurrence, and disease progression remain suboptimal. A significant factor contributing to these challenges is the cellular heterogeneity within BC, particularly the presence of breast cancer stem cells (BCSCs). These cells are thought to serve as the clonogenic nexus for new tumor growth, owing to their hierarchical organization within the tumor. This descriptive review focuses on the evolving strategies to target BCSCs, which have become a pivotal aspect of therapeutic development. We explore a variety of approaches, including targeting specific tumor surface markers (CD133 and CD44), transporters, heat shock proteins, and critical signaling pathways like Notch, Akt, Hedgehog, KLF4, and Wnt/ß-catenin. Additionally, we discuss the modulation of the tumor microenvironment through the CXCR-12/CXCR4 axis, manipulation of pH levels, and targeting hypoxia-inducible factors, vascular endothelial growth factor, and CXCR1/2 receptors. Further, this review focuses on the roles of microRNA expression, strategies to induce apoptosis and differentiation in BCSCs, dietary interventions, dendritic cell vaccination, oncolytic viruses, nanotechnology, immunotherapy, and gene therapy. We particularly focused on studies reporting identification of BCSCs, their unique properties and the efficacy of various therapeutic modalities in targeting these cells. By dissecting these approaches, we aim to provide insights into the complex landscape of BC treatment and the potential pathways for improving patient outcomes through targeted BCSC therapies.

Silica-Based Superhydrophobic and Superoleophilic Cotton Fabric with Enhanced Self-Cleaning Properties for Oil-Water Separation and Methylene Blue Degradation.

Superhydrophobic textiles with multifunctional characteristics are highly desired and have attracted tremendous research attention. This research employs a simple dip-coating method to obtain a fluorine-free silica-based superhydrophobic and superoleophilic cotton fabric. Pristine cotton fabric is coated with SiO2 nanoparticles and octadecylamine. SiO2 nanoparticles are anchored on the cotton fabric to increase surface roughness, and octadecyl amine lowers the surface energy, turning the hydrophilic cotton fabric into superhydrophobic. The designed cotton fabric exhibits a water contact angle of 159° and a sliding angle of 7°. The prepared cotton fabric is characterized by attenuated total reflectance-fourier transform infrared spectroscopy, X-ray diffraction, atomic force microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. In addition, the coated fabric reveals excellent features, including mechanical and chemical stability, superhydrophobicity, superoleophilicity, and the self-cleaning ability. SiO2 nanoparticles and octadecylamine-coated cotton fabric demonstrate exceptional oil-water separation and wastewater remediation performance by degrading the methylene blue solution up to 89% under visible light. The oil-water separation ability is tested against five different oils with more than 90% separation efficiency. This strategy has the advantages of low-cost precursors, a simple and scalable coating method, enhanced superhydrophobicity and superoleophilicity, self-cleaning ability, efficient oil-water separation, and exceptional wastewater remediation performance.

2D MXenes and their composites; design, synthesis, and environmental sensing applications.

Novel 2D layered MXene materials were first reported in 2011 at Drexel University. MXenes are widely used in multidisciplinary applications due to their anomalous electrical conductivity, high surface area, and chemical, mechanical, and physical properties. This review summarises MXene synthesis and applications in environmental sensing. The first section describes different methods for MXene synthesis, including fluorinated and non-fluorinated methods. MXene's layered structure, surface terminal groups, and the space between layers significantly impact its properties. Different methods to separate different MXene layers are also discussed using various intercalation reagents and commercially synthesized MXene without compromising the environment. This review also explains the effect of MXene's surface functionalization on its characteristics. The second section of the review describes gas and pesticide sensing applications of Mxenes and its composites. Its good conductivity, surface functionalization with negatively charged groups, intrinsic chemical nature, and good mechanical stability make it a prominent material for room temperature sensing of environmental samples, such as polar and nonpolar gases, volatile organic compounds, and pesticides. This review will enhance the young scientists' knowledge of MXene-based materials and stimulate their diversity and hybrid conformation in environmental sensing applications.

Recent Trends in S. aureus and E. coli-Based Endometritis, and the Therapeutic Evaluation of Sodium Alginate-Based Antibiotics and Nanoparticles.

Postpartum infection of the uterus by pathogenic bacteria is exacerbated due to a lack of sufficient epidemiological studies and evidence-based therapeutics. Therefore, this study was planned to find the prevalence, risk factors, and drug-resistance profile of S. aureus and E. coli isolated from bovine endometritis and to evaluate the antibacterial potential of sodium alginate-based antibiotics and nanoparticles. The study revealed 34.21% S. aureus and 31.57% E. coli, whereas most of the assumed risk factors presented significant association in this study. S. aureus showed the highest resistance against fusidic acid (60%) and cefoxitin (50%), while the highest resistance in E. coli was found against fusidic acid (60%), gentamicin (60%), chloramphenicol (50%), and cefoxitin (50%). Tylosin coupled with MgO nanoparticles stabilized in sodium alginate gel (Tylo + MgO + gel) presented significantly lower minimum inhibitory concentration (MIC) against E. coli, showing 13.88 ± 4.51 µg/mL after 24 h incubation. On the other hand, gel-based preparations showed MIC as 31.25 ± 0 µg/mL (Tylo + gel + MgO) and 26.04 ± 9.02 µg/mL (Tylo + Gel) against S. aureus. Generally, the MICs of non-gel-based preparations were significantly higher against bacteria except ampicillin against S. aureus in this study. The toxicity analysis of MgO nanoparticles presented 20-80% mortality of snails against a wider range of 0.01 mg/mL-10 mg/mL. The histopathological parameters concluded MgO nanoparticles safe to use on off targets. The current study thus concludes the rise in antimicrobial resistance while the gel-based products appearing as effective antimicrobials with sufficient safety margins for off-targets. The study thus invites further investigation for the development of suitable and affordable modified therapeutics for better health and production of animals.

COVID-19 Vaccination and Its Relation to New-Onset Diabetes: A Narrative Review.

The COVID-19 vaccination has been effective in preventing a lot of complications caused by SARS-CoV-2 and its variants. Meanwhile, diabetes mellitus, one of the root causes of many co-morbidities, exhibited itself during the COVID-19 pandemic and after COVID-19 vaccination. Diabetes mellitus introduced itself in a new perspective, leading to a variety of presentations and causing a significant number of emergency admissions. Many of the pre-diabetes patients with no prior history of diabetes developed fulminant type 1 diabetes mellitus (T1DM) after the COVID-19 vaccination. Some cases of conversion of type 2 diabetes mellitus (T2DM) into T1DM were reported. Some prediabetes/diabetes patients presented with the development of diabetic ketoacidosis after COVID-19 vaccination, whereas some previously healthy people with no relation to diabetes also developed acute exacerbations of new-onset T1DM or T2DM along with lethal ketoacidosis. The purpose of writing this review was to explore what kind of people are more prone to develop new-onset diabetes or diabetic complications, including diabetic ketoacidosis, the typical presentation of these patients, possible mechanisms that lead to these complications occurring after the COVID-19 vaccination, how they can be managed, and whether there is a good prognosis after management or not.

Knowledge, beliefs and hesitancy among medical and non-medical university students toward COVID-19 vaccination in Punjab, Pakistan.

This study assessed the knowledge, beliefs, and hesitancy toward COVID-19 vaccination among medical (MS) and nonmedical students (NMS) in Punjab, Pakistan. An online cross-sectional survey was conducted by enrolling 624 MS and 476 NMS in the largest province of Pakistan, i.e., Punjab. Chi-square tests were used to determine significant frequency distributions, and logistic regression to determine associations. The majority of students, males and females, were between 18-25 years of age and had good self-reported health. MS demonstrated adequate knowledge and positive beliefs about COVID-19. Fewer MS were indecisive about the vaccination; nonetheless, overall, 71.5% of MS and 78.2% of NMS were willing to receive the vaccine. A greater number of MS students were concerned about vaccine safety or side effects. A greater number of NMS demonstrated misconceptions about the COVID-19 vaccine - its effect on fertility and alterations in genomic DNA. The binary logistics regression exhibited a significant association between students' knowledge in the medical sciences (OR;1.53,p = 0.002) and area of residence (OR;1.60,p = 0.008). Compared to NMS, MS had a better understanding of the COVID-19 vaccine and were concerned about the acceptability of the vaccine, although NMS had misconceptions about the COVID-19 vaccine. Knowledge was significantly associated with the field of study.

Recent advancements in the fabrication and photocatalytic applications of graphitic carbon nitride-tungsten oxide nanocomposites.

The present review focuses on the widely used graphitic carbon nitride (g-C3N4)-tungsten oxide (WO3) nanocomposite in photocatalytic applications. These catalysts are widely employed due to their easy preparation, high physicochemical stability, nontoxicity, electron-rich properties, electronic band structure, chemical stability, low cost, earth-abundance, high surface area, and strong absorption capacity in the visible range. These sustainable properties make them predominantly attractive and unique from other photocatalysts. In addition, graphitic carbon nitride (g-C3N4) is synthesized from nitrogen-rich precursors; therefore, it is stable in strong acid solutions and has good thermal stability up to 600 °C. This review covers the historical background, crystalline phases, density-functional theory (DFT) study, synthesis method, 0-D, 1-D, 2-D, and 3-D materials, oxides/transition/nontransition metal-doped, characterization, and photocatalytic applications of WO3/g-C3N4. Enhancing the catalytic performance strategies such as composite formation, element-doping, heterojunction construction, and nanostructure design are also summarized. Finally, the future perspectives and challenges for WO3/g-C3N4 composite materials are discussed to motivate young researchers and scientists interested in developing environment-friendly and efficient catalysts.

Frequency of Gastroparesis Symptoms in Patients With Type-2 Diabetes Mellitus at a Tertiary Care Hospital in Pakistan.

Background Gastroparesis symptoms seem to affect many diabetes mellitus patients. Pakistan has a high prevalence of diabetes, with an estimated 33 million people living with the condition. This study aimed to investigate the frequency of gastroparesis symptoms in patients with type-2 diabetes mellitus (T2DM). Methods This cross-sectional study was conducted from April to May 2022 in the outpatient Department of Medicine at the University College of Medicine and Dentistry, Lahore, Pakistan. Patients of both genders aged between 18 and 75 years and having T2DM were analysed. Data regarding demographic information, medical history, BMI assessment, and symptoms related to gastroparesis, as measured by the Gastroparesis Cardinal Symptoms Index (GCSI) were collected at the time of enrolment. For gastroparesis, a diagnostic cutoff of ≥1.90 was considered as per GCSI. Results Of a total of 148 T2DM patients, 85 (57.4%) were females. The mean age was calculated to be 54.0±11.3 years, ranging between 23 and 75 years. There were 134 (90.5%) patients who were using metformin. The most common symptom was fullness, reported by 66 (44.6%), while nausea, bloating, early satiety, retching, and vomiting were noted in 61 (41.2%), 59 (39.9%), 55 (37.2%), 39 (26.4%), and 22 (14.9%) patients, respectively. The frequency of gastroparesis was found in 17 (11.5%) T2DM patients. Stratification of gastroparesis revealed a significant association with female gender (82.4% vs. 54.2%, p=0.0272). Conclusion The study found a high frequency of gastroparesis symptoms in patients with T2DM. Nausea and bloating were the most commonly reported symptoms, while vomiting was the least common. The prevalence of gastroparesis was significantly higher in the female gender.

Stearic Acid and CeO2 Nanoparticles Co-functionalized Cotton Fabric with Enhanced UV-Block, Self-Cleaning, Water-Repellent, and Antibacterial Properties.

Superhydrophobic cotton fabrics with multifunctional features are highly desired in domestic and outdoor applications. However, the short coating longevity and hazardous reagents significantly reduce their commercial-scale applications. Herein, we introduce CeO2 nanoparticles and stearic acid (SA) to develop a fluorine-free, durable superhydrophobic cotton fabric that mimics the lotus effect. The pristine cotton fabric is treated with APTES-functionalized CeO2 nanoparticles by immersion followed by a dip and drying treatment with a 2% myristic acid solution. This sequential process creates a stable superhydrophobic cotton fabric (SA/CeO2-cotton fabric) with a water contact angle of 158° and a water sliding angle of 5°. The results are attributed to the combined effect of CeO2 nanoparticles and stearic acid that enhances surface roughness and reduces surface sorption energy. APTES facilitates the durable attachment of CeO2 nanoparticles and stearic acid to the cotton fabric. The modified cotton fabric is characterized by advanced analytical tools, demonstrating enhanced superhydrophobicity, self-cleaning, and antiwater absorption properties. Additionally, it exhibits remarkable UV-blocking (UPF 542) and antibacterial properties. The designed superhydrophobic cotton fabric unveils good mechanical, thermal, and chemical durability. The proposed strategy is simple, green, and economical and can be used commercially for functional fabric preparation.

Usage of the XGBoost and MARS algorithms for predicting body weight in Kajli sheep breed.

This study aimed to utilize the XGBoost and MARS algorithms to predict present weight from body measurements. The algorithms have the potential to model nonlinear relationships between body measurements and weight, and this study attempted to find a model that provided the most accurate predictions of present weight. The current study was conducted with 152 animals in order to achieve a certain goal. To compare the model performances, goodness-of-fit criteria such as R2, r, RMSE, CV, SDratio, PI, MAPE, AIC were used. According to the results of this study, the XGBoost algorithm was the most reliable model for predicting present weight from body measurement. Even if the XGBoost algorithm was the most accurate model, the MARS algorithm was the reliable model for the same aim. In addition, it is hoped that the results of this study will help researchers and breeders better understand the relationship between body measurements and weight and ultimately be able to help individuals better manage their weight. As a conclusion, in the current study, the XGBoost algorithm is an effective, efficient, and reliable tool for accurately estimating present weight from body measurements. This makes it an invaluable tool in rural areas, where traditional weighing scales may not be available or reliable.

Knowledge, Attitudes, Perceptions, and Acceptance of COVID-19 Vaccination among Pharmacy and Non-Pharmacy Students.

University students are a sub-group of the population at high risk of COVID-19 infection, and their judgments on vaccination affect the public attitudes towards vaccination. Thus, the present study aimed to assess the knowledge, attitudes, perceptions, and acceptance of the COVID-19 vaccine among pharmacy and non-pharmacy students. A cross-sectional study was conducted by enrolling pharmacy (375) and non-pharmacy (225) students from the universities in Lahore. Chi-square analysis was used for significant frequency distributions and a 5-point Likert scale was used to score attitude, perception, and acceptance. The majority of the students were aged between 19-24 years, hailing from urban and middle-class families with good self-reported health. The preferred vaccine was Pfizer, followed by Sinopharm and Sinovac. The major source of information was social media, followed by government campaigns and family members. The pharmacy students demonstrated better knowledge and positive attitudes toward COVID-19 vaccination. The non-pharmacy students scored higher for the questions based on scientific leads, myths, and baffling conspiracies. The non-pharmacy students showed higher hesitancy/barrier total scores related to their trust in the health system, COVID-19 vaccine storage, and efficacy. Data suggested that pharmacy students exhibited better knowledge, positive attitudes, and perceptions about COVID-19 vaccination. Overall, vaccine efficacy and safety were mutual concerns. Nonetheless, non-pharmacy students were hesitant due to mistrust in the health system of Pakistan.

A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques.

Context and Background: Since December 2019, the coronavirus (COVID-19) epidemic has sparked considerable alarm among the general community and significantly affected societal attitudes and perceptions. Apart from the disease itself, many people suffer from anxiety and depression due to the disease and the present threat of an outbreak. Due to the fast propagation of the virus and misleading/fake information, the issues of public discourse alter, resulting in significant confusion in certain places. Rumours are unproven facts or stories that propagate and promote sentiments of prejudice, hatred, and fear. Objective. The study's objective is to propose a novel solution to detect fake news using state-of-the-art machines and deep learning models. Furthermore, to analyse which models outperformed in detecting the fake news. Method. In the research study, we adapted a COVID-19 rumours dataset, which incorporates rumours from news websites and tweets, together with information about the rumours. It is important to analyse data utilizing Natural Language Processing (NLP) and Deep Learning (DL) approaches. Based on the accuracy, precision, recall, and the f1 score, we can assess the effectiveness of the ML and DL algorithms. Results. The data adopted from the source (mentioned in the paper) have collected 9200 comments from Google and 34,779 Twitter postings filtered for phrases connected with COVID-19-related fake news. Experiment 1. The dataset was assessed using the following three criteria: veracity, stance, and sentiment. In these terms, we have different labels, and we have applied the DL algorithms separately to each term. We have used different models in the experiment such as (i) LSTM and (ii) Temporal Convolution Networks (TCN). The TCN model has more performance on each measurement parameter in the evaluated results. So, we have used the TCN model for the practical implication for better findings. Experiment 2. In the second experiment, we have used different state-of-the-art deep learning models and algorithms such as (i) Simple RNN; (ii) LSTM + Word Embedding; (iii) Bidirectional + Word Embedding; (iv) LSTM + CNN-1D; and (v) BERT. Furthermore, we have evaluated the performance of these models on all three datasets, e.g., veracity, stance, and sentiment. Based on our second experimental evaluation, the BERT has a superior performance over the other models compared.

Structured, Timely, and Individualized Patient Feedback to Residents and its Effect on HCAHPS Scores.

Physician communication is integral for improving patients' experience and clinical outcomes. Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores are used by the Centers for Medicare & Medicaid Services to assess the quality of care provided to the patients. We conducted a prospective pre-post-intervention study from March 2018 to March 2020 and included patients and residents of the community-based university-affiliated Internal Medicine Residency Program. Residents' communication was evaluated through resident surveys and results were shared with them and attending physicians within 48 h. To correlate with the effect over a 4-year period, 839 HCAHPS surveys were obtained for attending physicians as an outcome measure. The mean overall HCAHPS score and doctor's communication scores increased by 8.52 and 6.06 points post-intervention. The doctor's performance score for respect increased by 6.18 points, listening increased by 3.12 points, and explanation increased by 8.23 points. This study demonstrates that timely, structured, and individualized resident feedback in an academic medicine service can result in a sustainable increase in the attendings' overall HCAHPS and doctor's communication scores.

Dermatologic Manifestation of Acro-Ischemia Associated With COVID-19.

Background: The common dermatologic manifestations seen in patients with coronavirus disease 2019 (COVID-19) include morbilliform, pernio-like, urticarial, macular erythematous, vesicular, and papulosquamous disorders, as well as retiform purpura. Although cases of acro-ischemia have been demonstrated, they are not well studied or reported. Case Report: A 73-year-old male was admitted for acute hypoxic respiratory failure secondary to COVID-19 infection. During the patient's hospital course, his oxygen requirement progressively increased, and he developed painful, violaceous purpura on his right lower extremity digits. The patient was treated with therapeutic doses of enoxaparin and nitroglycerin ointment in the hospital and apixaban on discharge. The patient was lost to follow-up. Conclusion: The multiorgan dysfunction associated with COVID-19 includes dermatologic manifestations. This case illustrates that acro-ischemia can resolve with guideline-based medical treatment.

Impact of various watering regimes on physiological and hematological parameters in intensively kept Marecha (Camelus dromedarius) she-camels in summer season.

Background: Camel is vital to daily life as a source of food and means of transportation, and just as importantly, its milk and urine have been used as medicine for diverse ailments since ancient times for people living in the desert region. Aim: The current study was planned to investigate the effect of different watering regimes on physiological and hematological parameters of lactating Marecha camels during the summer season. Methods: For this purpose, 12 lactating she-camels of almost similar age (8-13 years) and parity (2-5) were selected in a completely randomized design with four animals per treatment at the Camel Breeding and Research Station Rakh Mahni District Bhakkar. The trial was completed in 60 days, while 7 days were given as an acclimatization period for adaptation. The treatments were as follows: Group-1 (G-1) considered as the control had access to water once in a day; Group-2 (G-2) had access to water once in 4 days, and Group-3 (G-3) had access to water once in 6 days. Animals were housed individually for feeding and watering in an intensive management system. The same feed consisting of green fodder (Lucerne) and gram straw (Cicer arientinum) was offered to all animals. The average ambient temperature and relative humidity were 39°C-41°C and 55%-63%, respectively. Result: The mean values (p < 0.001) of water intake were found to be 82.94 ± 1.34 l in G-3, which was higher than G-1 and G-2. Blood analysis showed that packed cell volume, white blood cells, and hemoglobin were affected significantly (p < 0.001, p < 0.01, and p < 0.05, respectively). Conclusion: No serious changes in complete blood count and physiological parameters were reported in this study. So, this shows that the camel is unique in its physiological adaptation in hot and hostile environments.

Effect of Different Watering Regimes in Summer Season on Water Intake, Feed Intake, and Milk Production of Marecha She-camel (Camelus dromedarius).

Twelve lactating healthy Marecha she-camels in the early lactation stage during the summer at Camel Breeding and Research Station Rakh-Mahni (Pakistan) were included. All animals were fed with Medicago sativa and Cicer arientinum ad libitum and divided into three groups in relation to the access to water, after a period of seven days of adaptation to experimental conditions. Group 1 (G1) was considered as control having access to water once every day; Group 2 (G2) had access once every 4 days, while Group 3 (G3) had access once every 6 days. The duration of the study was 60 days with an adaptation period to experimental conditions of 7 days before the onset of the study. Dry matter intake (DMI) was calculated on a dry matter basis. On average the ambient temperature and relative humidity during the trial were 39-41 °C and 55-63%, respectively. The DMI, water intake, milk production, and body weight changes were affected (p < 0.001) during various watering regimes. The mean values of water intake were found to be 82.94 ± 1.34 L higher in G3 than G1 and G2.

Use of multivariate adaptive regression splines for prediction of body weight from body measurements in Marecha (Camelus dromedaries) camels in Pakistan.

The aim of the present work was to predict live body weight by means of some body measurements, i.e., SH, CG, and BG in indigenous Marecha camel breed. For this purpose, multivariate adaptive regression splines (MARS) algorithm was used at proportions of various training and test sets, i.e., 65:35, 70:30, and 80:20 in V-tenfold cross-validation. In prediction of live body weight of the Marecha camels (160 female and 145 male animals) in the MARS predictive models, pairs of sex-SH (model 1), sex-CG (model 2), and sex-BG (model 3) as potential predictors. The best MARS model in LW prediction was obtained using sex and SH independent variables for 80:20 training and test set. Sex was determined to be an important source of variation in SH, CG, and BG as a result of sexual dimorphism in camels (P < 0.01). MARS results indicated that SH could be used as an indirect selection criterion to obtain elite camel herds on LW of Marecha camels. If genetically confirmed, the Marecha camels whose SH is taller than 165.1 cm could be selected for providing genetic progress in LW. In conclusion, use of MARS algorithm may be worthy of consideration for better identification of camel breed standards and selection of superior Marecha camels for meat productivity in Pakistan.

Predictors of Disease Severity in Adult Covid-19 Patients Admitted in Mayo Hospital, Lahore, Pakistan.

OBJECTIVE: To assess disease severity, based on clinical presentation and laboratory investigations. STUDY DESIGN:  Observational study. PLACE AND DURATION OF STUDY: COVID-19 Isolation Unit of Mayo Hospital, Lahore from 15th March to 31st May, 2020. METHODOLOGY: Four hundred and forty-five COVID-19 RT-PCR positive patients of either gender in age group of 18-80 years, admitted in isolation wards, high dependency units (HDUs) and intensive care units (ICUs) of the Hospital, were selected for the study via simple random sampling. Clinical presentations and laboratory investigations were recorded for all patients. Kruskal-Wallis test, Pearson Chi-square test, boxplots and ROC curve were used to analyse the data. A p-value ≤0.05 was considered statistically significant. RESULTS: Out of the 445 cases, a male predominance 286 (64.3%) was observed with majority of patients 324 (72.8%) having mild disease, 73 (16.4%) moderate, 25 (5.6%) severe and 23 (5.2%) having critical disease. D-dimer was considered to be the best discriminatory marker to assess disease severity with an overall accuracy of 92.1%. CONCLUSION: Fever, sore throat, shortness of breath, body aches, abdominal pain, anosmia and aguesia were the predominant symptoms in majority of patients belonging to different categories based on disease severity. Inflammatory markers like D-dimers and ferritin levels determined the overall disease severity with a high accuracy. Key Words: Disease severity, Clinical presentation, Laboratory investigations, RT-PCR.

Prediction of live body weight based on body measurements in Thalli sheep under tropical conditions of Pakistan using cart and mars.

Thalli sheep is a significant breed reared under tropical region of Punjab province of Pakistan. The present study was conducted to predict live body weight (LBW) by means of from some body measurements, i.e., chest girth (CG), belly girth (BG), rump height (RH), withers height (WH), neck girth (NG), and body length (BL) taken from 155 Thalli indigenous sheep of Pakistan. Age factor is determined to be a significant source of variation for BL, BG, CG, BG, WH, and NG (p < 0.05). LBW is correlated significantly with BL (0.850), CG (0.825), BG (0.849), RH (0.579), WH (0.547), and NG (0.7760), respectively (p < 0.01). For LBW prediction, CART and MARS data mining algorithms were comparatively used based on ten cross-validation method. Among 185 candidate MARS models with 1-5 degrees of interaction and 2-38 terms, the MARS model with 7 terms and no interaction effect in R software was the best model for LBW prediction on the basis of the smallest cross-validated RMSE value. Also, the optimal CART tree structure was obtained with 9 terminal nodes for the smallest cross-validated RMSE value. MARS algorithm outperformed CART in LBW prediction and explained 90.3 (%) of variability in LBW of Thalli sheep. Results of the optimal CART structure reflected that Thalli sheep with BL > 75 cm, RH > 83 cm, and NG > 55 cm has the heaviest LBW of 72 kg. The optimal MARS model displays that the heaviest LBW can be produced by Thalli sheep with BL > 71.12 cm, BG > 106.68 cm, WH > 76.2 cm, NG > 50.8 cm in 5th age group. In conclusion, it coud be recommended that MARS predictive modeling may enable animal breeders to obtain elite Thalli sheep population and to detect body measurement positively influencing LBW as indirect selection criteria for not only describing breed characterization and developing flock management standards, but also ensuring sustainable meat production and rural development in Pakistan.

Pregnancy status and thyroid function in semi-intensive-kept Marecha she-camels (Camelus dromedarius): managerial implications.

Pakistani Marecha dromedaries, a distinctive breed not covered in the empirical data of the literature reviewed existing for thyroidal physiology in camels, are subjected to investigate thyroid function in pregnant multiparous (n = 22) and non-pregnant nor lactating (n = 22) females. The function of the thyroid gland in pubescent she-camels has evoked discrepancies among researchers exploring this topic in different breeds evolving in divergent environments. Despite season, age, sex, reproductive status, health disorders, and stress are reported as potential modulating factors, these research studies failed to find statistically significant results supporting their hypothesis. For the current research, serum samples were obtained twice a week during one month from semi-intensive-kept Marecha she-camels and analyzed for triiodothyronine (T3) and thyroxine (T4) concentrations. Feed ingredients and additives were quality assessed to examine their nutrient profile, with special attention to protein content and exogenous calcium administration. Statistical differences in serum thyroid hormone levels were found between gravid and non-gravid females, being pregnant she-camels the subgroup with higher levels (P < 0.05). The genetic background of such phenotypic variability in thyroid metabolism in camels has to be further addressed to adapt husbandry practices for breed-specific requirements. Meanwhile, local management strategies are susceptible of accurate routines for sustainable farming.