Phenotypic heterogeneity is increasingly appreciated to confer several advantages to cancer progression and drug resistance. Here we probe the collective importance of heterogeneity in cell size and deformability in breast cancer invasion. A computational model of invasion of a heterogeneous cell aggregate predicts that combined heterogeneity in cell size and deformability enhances invasiveness of the whole population, with maximum invasiveness at intermediate cell-cell adhesion. We then show that small cells of varying deformability—a sub-population predicted to be enriched at the invasive front—exhibit considerable overlap with biophysical properties of cancer stem cells (CSCs). In MDA-MB-231 cells, these include CD44hiCD24− mesenchymal CSCs which are small and soft, and CD44hiCD24+ hybrid CSCs which exhibit a wide range of size and deformability. We validate our predictions by tracking pattern of cell invasion from spheroids implanted in 3D collagen gels, wherein we show temporal enrichment of CD44hi cells at the invasive front. Collectively, our results illustrate the advantages imparted by biophysical heterogeneity in enhancing cancer invasiveness.

The objective of this study was to conduct a qualitative analysis of raw beef meat sold in the city of Quetta, Pakistan for presence and drug sensitivity of the potentially pathogenic Escherichia coli strain O157:H7. The study used 200 raw beef meat samples collected from retail butcher shops. Conventional and rapid biochemical tests, latex agglutination and multiplex polymerase chain reaction (PCR) using primers designed for the rfb O157 and flic H7 genes were used to detect E. coli O157:H7. All O157:H7 isolates were also tested for Shiga toxin genes stx 1 and stx 2. The prevalence of E. coli O157:H7 in collected beef meat samples was 10%. Detection through PCR was found more sensitive than detection of O and H antigens. The quantity of E. coli O157:H7 isolates positive for Shiga toxins was 50% (20% for stx 1, 45% for stx 2 and 10% for both stx 1 and stx 2). Season wise variation showed highest E. coli O157:H7 prevalence during summer months. A further concern is that E. coli O157:H7 isolates were resistant to a range of common antibiotics. The results indicate an urgent need for applying proper food hygiene practices in the Quetta region to reduce incidence of foodborne diseases and they also emphasize the global problem of antimicrobial resistance. Practical applications E. coli O157:H7 is as a potentially threatening foodborne pathogen. A significant prevalence of E. coli O157:H7 detected in raw beef meat from retail outlets in the city of Quetta indicates an urgent need for applying proper food hygiene practices in the Quetta region to reduce the incidence of foodborne diseases. Furthermore, resistance of the E. coli O157:H7 isolates to a range of commonly used antibiotics emphasizes the global problem of antimicrobial resistance. The multiplex PCR method used here is a reliable, sensitive, and relatively rapid technique for detecting E. coli O157:H7 in food and environmental samples and important for ongoing surveillance to minimize contamination of raw meat products and associated cross contamination by E. coli O157:H7.

Present study is based on 20 methicillin-resistant Staphylococcus aureus (MRSA) isolates recovered from different food items. These isolates were identified on the basis of colony morphology, Gram staining and growth on different selective and differential media. Studies on 16S RNA and positive reactions on DNase agar and Prolex Latex Agglutination system confirm it as Staphylococcus aureus. Oxacillin susceptibility testing and PCR with mecA gene-specific primer results showed that these isolates are MRSA-carrying mecA gene that belongs to SCCmecA type IV and also harbor agr type II. Phenotypic study revealed that these isolates adopt biofilm mode of growth after exposure to subinhibitory doses of oxacillin. The biofilm and cell surface hydrophobicity have a strong correlation. It was noticed that affinity to hexadecane (apolar-solvent) of planktonic cells was low, suggesting its hydrophilic character. However, as the cells are exposed to oxacillin, they adopt biofilm mode of life and the affinity to apolar solvent increases, indicating a hydrophobic character. In biofilm consortia, the cells with more hydrophobic surfaces show incomplete septation and produce multicellular aggregates. This is due to reduced expression of atl gene. This was confirmed by real-time PCR studies. Moreover, the planktonic or wild-type phenotypes of these isolates were more tolerant to antibacterial effect of the fatty acids used; that is, cis-2-decanoic acid and cis-9-octadectanoic acid. These fatty acids were more effective against biofilms. After exposure to these fatty acids, established biofilms were dispersed and surviving cells were unable to readopt biofilm mode of life. The planktonic or wild-type phenotypes produce fatty acid-modifying enzyme (FAME) to inactivate the bactericidal activity of fatty acids by esterification to cholesterol. The biofilm indwellers are metabolically inactive and unable to produce FAME; hence, they are vulnerable to antibiofilm effect of cis-2-decanoic acid and cis-9-octadectanoic acid.

The COVID-19 outbreak resulted in preventative measures and restrictions for Bangladesh during the summer of 2020—these unstable and stressful times led to multiple social problems (e.g., domestic violence and divorce). Globally, researchers, policymakers, governments, and civil societies have been concerned about the increase in domestic violence against women and children during the ongoing COVID-19 pandemic. In Bangladesh, domestic violence against women and children has increased during the COVID-19 pandemic. In this article, we investigated family violence among 511 families during the COVID-19 outbreak. Participants were given questionnaires to answer, for a period of over ten days; we predicted family violence using a machine learning-based model. To predict domestic violence from our data set, we applied random forest, logistic regression, and Naive Bayes machine learning algorithms to our model. We employed an oversampling strategy named the Synthetic Minority Oversampling Technique (SMOTE) and the chi-squared statistical test to, respectively, solve the imbalance problem and discover the feature importance of our data set. The performances of the machine learning algorithms were evaluated based on accuracy, precision, recall, and F-score criteria. Finally, the receiver operating characteristic (ROC) and confusion matrices were developed and analyzed for three algorithms. On average, our model, with the random forest, logistic regression, and Naive Bayes algorithms, predicted family violence with 77%, 69%, and 62% accuracy for our data set. The findings of this study indicate that domestic violence has increased and is highly related to two features: family income level during the COVID-19 pandemic and education level of the family members.

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