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Passive thermoregulatory textiles, operating without external energy input, play a crucial role in maintaining the human body within the thermal comfort zone. However, integrating autonomous environmental adaptability with superior wearing comfort into a single textile remains a challenge. In this work, inspired by the autonomous actuation of water lilies, we proposed an intelligent strategy to fabricate thermoregulatory textiles that dynamically adapted to ambient temperature fluctuations, driven by a bidirectional shape memory polymer (SMP). To concurrently achieve robust thermal adaptability and human-body-compatible softness, a crosslinked polyethylene glycol–butyl acrylate (PEG-BA) bidirectional SMP network was engineered. The PEG phase, featuring a broad crystal size distribution, provided the dynamic skeleton for thermally induced actuation, while the incorporation of the BA component tuned the intrinsic softness to match conventional soft textiles. Consequently, the synthesized PEG-BA network exhibited an exceptional bidirectional shape memory effect with a reversible strain of 15.5%, while maintaining high macroscopic softness comparable to that of human skin. By integrating this bidirectional polymer into a garment to form adaptive vents, the smart textile demonstrated the capability to significantly elevate human thermal comfort. Specifically, the vents autonomously open in hot environments to accelerate heat dissipation and close in cool environments to suppress heat loss. Given its exceptional personal thermoregulatory performance and wearing compliance, this proposed strategy exhibits considerable potential for maintaining optimal human comfort against fluctuating environmental conditions.

Background CYP2C8 is responsible for the metabolism of 5% of clinically prescribed drugs, including antimalarials, anti-cancer and anti-inflammatory drugs. Genetic variability is an important factor that influences CYP2C8 activity and modulates the pharmacokinetics, efficacy and safety of its substrates. Results We profiled the genetic landscape of CYP2C8 variability using data from 96 original studies and data repositories that included a total of 33,185 unrelated participants across 44 countries and 43 ethnic groups. The reduced function allele CYP2C8*2 was most common in West and Central Africa with frequencies of 16–36.9%, whereas it was rare in Europe and Asia (< 2%). In contrast, CYP2C8*3 and CYP2C8*4 were common throughout Europe and the Americas (6.9–19.8% for *3 and 2.3–7.5% for *4 ), but rare in African and East Asian populations. Importantly, we observe pronounced differences (> 2.3-fold) between neighboring countries and even between geographically overlapping populations. Overall, we found that 20–60% of individuals in Africa and Europe carry at least one CYP2C8 allele associated with reduced metabolism and increased adverse event risk of the anti-malarial amodiaquine. Furthermore, up to 60% of individuals of West African ancestry harbored variants that reduced the clearance of pioglitazone, repaglinide, paclitaxel and ibuprofen. In contrast, reduced function alleles are only found in < 2% of East Asian and 8.3–12.8% of South and West Asian individuals. Conclusions Combined, the presented analyses mapped the genetic and inferred functional variability of CYP2C8 with high ethnogeographic resolution. These results can serve as a valuable resource for CYP2C8 allele frequencies and distribution estimates of CYP2C8 phenotypes that could help identify populations at risk upon treatment with CYP2C8 substrates. The high variability between ethnic groups incentivizes high-resolution pharmacogenetic profiling to guide precision medicine and maximize its socioeconomic benefits, particularly for understudied populations with distinct genetic profiles.

Both safety and efficacy of medical treatment can vary depending on the ethnogeographic background of the patient. One of the reasons underlying this variability is differences in pharmacogenetic polymorphisms in genes involved in drug disposition, as well as in drug targets. Knowledge and appreciation of these differences is thus essential to optimize population-stratified care. Here, we provide an extensive updated analysis of population pharmacogenomics in ten pharmacokinetic genes (CYP2D6, CYP2C19, DPYD, TPMT, NUDT15 and SLC22A1), drug targets (CFTR) and genes involved in drug hypersensitivity (HLA-A, HLA-B) or drug-induced acute hemolytic anemia (G6PD). Combined, polymorphisms in the analyzed genes affect the pharmacology, efficacy or safety of 141 different drugs and therapeutic regimens. The data reveal pronounced differences in the genetic landscape, complexity and variant frequencies between ethnogeographic groups. Reduced function alleles of CYP2D6, SLC22A1 and CFTR were most prevalent in individuals of European descent, whereas DPYD and TPMT deficiencies were most common in Sub-Saharan Africa. Oceanian populations showed the highest frequencies of CYP2C19 loss-of-function alleles while their inferred CYP2D6 activity was among the highest worldwide. Frequencies of HLA-B*15:02 and HLA-B*58:01 were highest across Asia, which has important implications for the risk of severe cutaneous adverse reactions upon treatment with carbamazepine and allopurinol. G6PD deficiencies were most frequent in Africa, the Middle East and Southeast Asia with pronounced differences in variant composition. These variability data provide an important resource to inform cost-effectiveness modeling and guide population-specific genotyping strategies with the goal of optimizing the implementation of precision public health.

Prediction of phenotypic consequences of mutations constitutes an important aspect of precision medicine. Current computational tools mostly rely on evolutionary conservation and have been calibrated on variants associated with disease, which poses conceptual problems for assessment of variants in poorly conserved pharmacogenes. Here, we evaluated the performance of 18 current functionality prediction methods leveraging experimental high-quality activity data from 337 variants in genes involved in drug metabolism and transport and found that these models only achieved probabilities of 0.1–50.6% to make informed conclusions. We therefore developed a functionality prediction framework optimized for pharmacogenetic assessments that significantly outperformed current algorithms. Our model achieved 93% for both sensitivity and specificity for both loss-of-function and functionally neutral variants, and we confirmed its superior performance using cross validation analyses. This novel model holds promise to improve the translation of personal genetic information into biological conclusions and pharmacogenetic recommendations, thereby facilitating the implementation of Next-Generation Sequencing data into clinical diagnostics.

Background Inter-individual differences in dihydropyrimidine dehydrogenase ( DPYD encoding DPD) and thiopurine S-methyltransferase (TPMT) activity are important predictors for fluoropyrimidine and thiopurine toxicity. While several variants in these genes are known to decrease enzyme activities, many additional genetic variations with unclear functional consequences have been identified, complicating informed clinical decision-making in the respective carriers. Methods We used a novel pharmacogenetically trained ensemble classifier to analyse DPYD and TPMT genetic variability based on sequencing data from 138,842 individuals across eight populations. Results The algorithm accurately predicted in vivo consequences of DPYD and TPMT variants (accuracy 91.4% compared to 95.3% in vitro). Further analysis showed high genetic complexity of DPD deficiency, advocating for sequencing-based DPYD profiling, whereas genotyping of four variants in TPMT was sufficient to explain >95% of phenotypic TPMT variability. Lastly, we provided population-scale profiles of ethnogeographic variability in DPD and TPMT phenotypes, and revealed striking interethnic differences in frequency and genetic constitution of DPD and TPMT deficiency. Conclusion These results provide the most comprehensive data set of DPYD and TPMT variability published to date with important implications for population-adjusted genetic profiling strategies of fluoropyrimidine and thiopurine risk factors and precision public health.

Background Variability in genes implicated in drug pharmacokinetics or drug response can modulate treatment efficacy or predispose to adverse drug reactions. Besides common genetic polymorphisms, recent sequencing projects revealed a plethora of rare genetic variants in genes encoding proteins involved in drug metabolism, transport, and response. Results To understand the global importance of rare pharmacogenetic gene variants, we mapped the variability in 208 pharmacogenes by analyzing exome sequencing data from 60,706 unrelated individuals and estimated the importance of rare and common genetic variants using a computational prediction framework optimized for pharmacogenetic assessments. Our analyses reveal that rare pharmacogenetic variants were strongly enriched in mutations predicted to cause functional alterations. For more than half of the pharmacogenes, rare variants account for the entire genetic variability. Each individual harbored on average a total of 40.6 putatively functional variants, rare variants accounting for 10.8% of these. Overall, the contribution of rare variants was found to be highly gene- and drug-specific. Using warfarin, simvastatin, voriconazole, olanzapine, and irinotecan as examples, we conclude that rare genetic variants likely account for a substantial part of the unexplained inter-individual differences in drug metabolism phenotypes. Conclusions Combined, our data reveal high gene and drug specificity in the contributions of rare variants. We provide a proof-of-concept on how this information can be utilized to pinpoint genes for which sequencing-based genotyping can add important information to predict drug response, which provides useful information for the design of clinical trials in drug development and the personalization of pharmacological treatment.

BackgroundCompared with western countries, Asian breast cancer patients have unique pathological and biological characteristics. Most of them are premenopausal women with HR positive. Tamoxifen as the first-line drug for premenopausal women with HR+ is involved in multiple enzymes and transporters during metabolizing and transporting process. Variants that cause decreased or inactive gene products leading to abnormal responses in tamoxifen therapy have well been studied in western countries, whereas such information is much less reported in Asian populations.ObjectiveIn order to elucidate the relationship between genetic variants and tamoxifen-induced individual drug reactions in different Asian populations and further identify genotypes/phenotypes with potential therapeutic significance.MethodsWe reviewed the frequencies of genetic variants in major enzymes and transporter genes involved in the metabolism and transport of tamoxifen across Asian populations as well as significant correlations between genotypes/metabolic phenotypes and metabolites concentrations or BC clinical outcomes.ResultsSignificant inter-ethnic differences in allele frequencies was found among Asian populations, such as CYP2D6*4, *10, *41, CYP2C9*2, ABCB1 C3435T and SLCO1B1*5, and CYP2D6*10/*10 is the most common genotype correlated with adverse clinical outcomes. Moreover, we summarized the barriers and controversies of implementing pharmacogenetics in tamoxifen therapy and concluded that more population-specific pharmacogenetic studies are needed in the future.ConclusionThis review revealed more systematic pharmacogenomics of genes involved in the metabolism and transport besides CYP2D6, are required to optimize the genotyping strategies and guide the personalized tamoxifen therapy in Asian populations.

ATP-binding cassette (ABC) transporters constitute a superfamily of 48 structurally similar membrane transporters that mediate the ATP-dependent cellular export of a plethora of endogenous and xenobiotic substances. Importantly, genetic variants in ABC genes that affect gene function have clinically important effects on drug disposition and can be predictors of the risk of adverse drug reactions and efficacy of chemotherapeutics, calcium channel blockers, and protease inhibitors. Furthermore, loss-of-function of ABC transporters is associated with a variety of congenital disorders. Despite their clinical importance, information about the frequencies and global distribution of functionally relevant ABC variants is limited and little is known about the overall genetic complexity of this important gene family. Here, we systematically mapped the genetic landscape of the entire human ABC superfamily using Next-Generation Sequencing data from 138,632 individuals across seven major populations. Overall, we identified 62,793 exonic variants, 98.5% of which were rare. By integrating five computational prediction algorithms with structural mapping approaches using experimentally determined crystal structures, we found that the functional ABC variability is extensive and highly population-specific. Every individual harbored between 9.3 and 13.9 deleterious ABC variants, 76% of which were found only in a single population. Carrier rates of pathogenic variants in ABC transporter genes associated with autosomal recessive congenital diseases, such as cystic fibrosis or pseudoxanthoma elasticum, closely mirrored the corresponding population-specific disease prevalence, thus providing a novel resource for rare disease epidemiology. Combined, we provide the most comprehensive, systematic, and consolidated overview of ethnogeographic ABC transporter variability with important implications for personalized medicine, clinical genetics, and precision public health.

Background Genetic variability in the cytochrome P450 CYP2C9 constitutes an important predictor for efficacy and safety of various commonly prescribed drugs, including coumarin anticoagulants, phenytoin and multiple non-steroidal anti-inflammatory drugs (NSAIDs). A global map of CYP2C9 variability and its inferred functional consequences has been lacking. Results Frequencies of eight functionally relevant CYP2C9 alleles ( *2 , *3 , *5 , *6 , *8 , *11 , *13 and *14 ) were analyzed. In total, 108 original articles were identified that included genotype data from a total of 81,662 unrelated individuals across 70 countries and 40 unique ethnic groups. The results revealed that CYP2C9*2 was most abundant in Europe and the Middle East, whereas CYP2C9*3 was the main reason for reduced CYP2C9 activity across South Asia. Our data show extensive variation within superpopulations with up to tenfold differences between geographically adjacent populations in Malaysia, Thailand and Vietnam. Translation of genetic CYP2C9 variability into functional consequences indicates that up to 40% of patients in Southern Europe and the Middle East might benefit from warfarin and phenytoin dose reductions, while 3% of patients in Southern Europe and Israel are recommended to reduce starting doses of NSAIDs. Conclusions This study provides a comprehensive map of the genetic and functional variability of CYP2C9 with high ethnogeographic resolution. The presented data can serve as a useful resource for CYP2C9 allele and phenotype frequencies and might guide the optimization of genotyping strategies, particularly for indigenous and founder populations with distinct genetic profiles.

Accurate prediction of tropical cyclone (TC) intensity is challenging due to the complex physical processes involved. Here, we introduce a new TC intensity prediction scheme for the western North Pacific (WNP) based on a time-dependent theory of TC intensification, termed the energetically based dynamical system (EBDS) model, together with the use of a long short-term memory (LSTM) neural network. In time-dependent theory, TC intensity change is controlled by both the internal dynamics of the TC system and various environmental factors, expressed as environmental dynamical efficiency. The LSTM neural network is used to predict the environmental dynamical efficiency in the EBDS model trained using best-track TC data and global reanalysis data during 1982–2017. The transfer learning and ensemble methods are used to retrain the scheme using the environmental factors predicted by the Global Forecast System (GFS) of the National Centers for Environmental Prediction during 2017–21. The predicted environmental dynamical efficiency is finally iterated into the EBDS equations to predict TC intensity. The new scheme is evaluated for TC intensity prediction using both reanalysis data and the GFS prediction data. The intensity prediction by the new scheme shows better skill than the official prediction from the China Meteorological Administration (CMA) and those by other state-of-art statistical and dynamical forecast systems, except for the 72-h forecast. Particularly at the longer lead times of 96 h and 120 h, the new scheme has smaller forecast errors, with a more than 30% improvement over the official forecasts.