Pin Pin Tea-Makorn

Multimorbidity, defined as the presence of two or more chronic conditions, is a growing public health concern. Understanding disease clustering and risk profiles is crucial for prevention and planning purposes. This study analyzed data from the National Health Survey in Thailand, which included 142,753 participants. Latent class analysis investigated nine non-communicable diseases, while multinomial logistic regression explored the characteristics associated with class membership. Four distinct classes were identified: low burden, hypertension–metabolic, predominant hypertension, and high multimorbidity. Advancing age revealed an increased probability of membership in all multimorbidity classes. Females exhibited a higher propensity to belong to the hypertension–metabolic and predominant hypertension classes. Urban residency was associated with an elevated probability of membership in the hypertension–metabolic and high-multimorbidity classes. The body mass index displayed significant variations across classes. Overweight and obesity were linked to the hypertension–metabolic and predominant hypertension classes, with obesity also associated with the high multimorbidity class. Underweight individuals demonstrated a reduced likelihood of membership in the hypertension–metabolic class but an increased likelihood of membership in the predominant hypertension class. Smoking was correlated with a higher probability of membership across classes, particularly among current smokers. Dietary behaviors were distinctly associated across classes. Frequent consumption of high-fat and instant foods was linked to the hypertension–metabolic class, whereas instant food consumption was also associated with the predominant hypertension class. Fruit consumption was correlated with a higher probability of membership in the hypertension–metabolic class, whereas vegetable consumption was associated with a reduced likelihood of membership in the predominant hypertension class. Moderate consumption of non-alcoholic sugary drinks was associated with a lower probability of hypertension–metabolic class membership. These findings underscore the heterogeneity of multimorbidity patterns in Thailand and delineate the demographic, behavioral, and residential profiles associated with non-communicable disease clustering.

This study proposes a comprehensive MedTech product innovation development framework tailored for university research commercialization within emerging markets. The MediGate framework, built on the Augmented Stage-Gate model, addresses the unique challenges of MedTech innovation, including regulatory compliance, stakeholder engagement, and market dynamics. The framework integrates critical decision-making criteria for different types of inventions to drive academic research toward commercialization in clinical settings. Through detailed case studies, including innovations like albumin strip test, 3D-printed patient-specific implant, COVID-19 nasal spray, and AI platform for depression detection, and iterative refinement, the framework provides actionable guidelines for navigating the complexities of product development. These guidelines ensure alignment with clinical needs, regulatory requirements, and market strategies. The research highlights the importance of early-stage valuation, reimbursement strategies, legal and IP considerations, and manufacturing and quality management. By offering a structured pathway, this research contributes to the theoretical and practical understanding of MedTech commercialization, aiming to enhance innovation success and healthcare impact in emerging markets.

Data analytics has recently gained significant momentum in tackling various challenges, climate change included. This work explores the sector, funding, and innovation landscape of climate startups that leverage data analytics. It applies a recently developed natural language processing algorithm, BERTopic, to organically discover sectors from company descriptions and innovation categories from patent abstracts. We classified data analytics climate startups into 37 sectors, with agriculture being the most prolific. We found that the state of California alone has more of these startups than any other country in the world; most funding rounds were raised in the Seed stage, and most companies exited by acquisition; most of the innovations generated are related to automation security, while the electric vehicle sector is most prolific in creating innovation. Investors, analysts, and entrepreneurs can use these methods and results to process large datasets and aid decision-making in investments and climate-related policies.

The convergence in physical appearance hypothesis posits that long-term partners’ faces become more similar with time as a function of the shared environment, diet, and synchronized facial expressions. While this hypothesis has been widely disseminated in psychological literature, it is supported by a single study of 12 married couples. Here, we examine this hypothesis using the facial images of 517 couples taken at the beginning of their marriage and 20 or more years later. Their facial similarity is estimated using two independent methods: human judgments and a facial recognition algorithm. The results show that while spouses’ faces tend to be similar at marriage, they do not converge over time. In fact, they become slightly less similar. These findings bring facial appearance in line with other personal characteristics—such as personality, intelligence, interests, attitudes, values, and well-being—through which spouses show initial similarity but no convergence over time.