A novel, portable, and cost-effective turbidimetric sensor for sensitive alkaline phosphatase activity assay.

The development of enzyme activity analysis methods is critical for precise and rapid assessments of enzyme activity levels within biological systems, facilitating a more profound comprehension of physiological functions and disease mechanisms. Alkaline phosphatase (ALP) participates in various physiological processes involving phosphate ester hydrolysis. Altered ALP activity levels are often indicative of different diseases, underscoring the necessity for accurate ALP activity determination in medical diagnostics. This study innovatively applies turbidity as a physical variable, proposing a turbidimetric sensor based on an enhanced ammonium molybdate reagent for phosphate analysis. By integrating this with the ALP substrate p-nitrophenyl phosphate, a turbidimetric sensor was devised and employed for ALP activity analysis. The proposed turbidimetric sensor demonstrated high sensitivity both for phosphate (0.18 µmol/L) and ALP activity (0.03 mU/mL) assay. In practical applications, this turbidimetric sensor has been effectively employed to detect ALP activity in mouse feces, showcasing its potential for auxiliary diagnosis of inflammatory bowel disease. Significantly, this novel turbidity-based approach offers not only swift and straightforward procedures but also remarkable portability and cost-efficiency. Requiring solely a handheld turbidimeter and eliminating the need for bulky instruments, this approach holds significant potential for point-of-care testing applications.

Temporally and spatially resolved molecular profiling in fingerprint analysis using indium vanadate nanosheets-assisted laser desorption ionization mass spectrometry.

This study presents the first-ever synthesis of samarium-doped indium vanadate nanosheets (IVONSs:Sm) via microemulsion-mediated solvothermal method. The nanosheets were subsequently utilized as a nano-matrix in laser desorption/ionization mass spectrometry (LDI-MS). It was discovered that the as-synthesized IVONSs:Sm possessed the following advantages: improved mass spectrometry signal, minimal matrix-related background, and exceptional stability in negative-ion mode. These qualities overcame the limitations of conventional matrices and enabled the sensitive detection of small biomolecules such as fatty acids. The negative-ion LDI mechanism of IVONSs:Sm was examined through the implementation of density functional theory simulation. Using IVONSs:Sm-assisted LDI-MS, fingerprint recognitions based on morphology and chemical profiles of endogenous/exogenous compounds were also achieved. Notably, crucial characteristics such as the age of an individual's fingerprints and their physical state could be assessed through the longitudinal monitoring of particular biomolecules (e.g., ascorbic acid, fatty acid) or the specific biomarker bilirubin glucuronide. Critical information pertinent to the identification of an individual would thus be facilitated by the analysis of the compounds underlying the fingerprint patterns.

Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data.

Investigators have recently introduced powerful methods for population genetic inference that rely on supervised machine learning from simulated data. Despite their performance advantages, these methods can fail when the simulated training data does not adequately resemble data from the real world. Here, we show that this "simulation mis-specification" problem can be framed as a "domain adaptation" problem, where a model learned from one data distribution is applied to a dataset drawn from a different distribution. By applying an established domain-adaptation technique based on a gradient reversal layer (GRL), originally introduced for image classification, we show that the effects of simulation mis-specification can be substantially mitigated. We focus our analysis on two state-of-the-art deep-learning population genetic methods-SIA, which infers positive selection from features of the ancestral recombination graph (ARG), and ReLERNN, which infers recombination rates from genotype matrices. In the case of SIA, the domain adaptive framework also compensates for ARG inference error. Using the domain-adaptive SIA (dadaSIA) model, we estimate improved selection coefficients at selected loci in the 1000 Genomes CEU population. We anticipate that domain adaptation will prove to be widely applicable in the growing use of supervised machine learning in population genetics.

Autophagy-Interfering Nanoboat Drifting along CD44-Golgi-ER Flow as RNAi Therapeutics for Hepatic Fibrosis.

The upregulated autophagy fuels the activation of hepatic stellate cells (HSCs) to promote hepatic fibrosis. However, the lack of specific inhibitors targeting autophagy and high requirements for cell targeting impede the application of antifibrotic therapy that targets autophagy. RNA interference (RNAi)-based short interfering RNA (siRNA) provides an approach to specifically inhibit autophagy. The therapeutic potential of siRNA, however, is far from being exploited due to the lack of safe and effective delivery vehicles. The cytoplasmic delivery of siRNA is essential for RNAi, and the intracellular trafficking pathway of vehicles determines the fate of siRNA. Unfortunately, the lysosomal degradation pathway, the intracellular fate of most gene vehicles, impedes RNAi efficiency. Inspired by the trafficking pathway of some viruses infecting cells, KDEL-grafted chondroitin sulfate (CK) was designed to alter the intracellular delivery fate of siRNA. The well-designed CD44-Golgi-ER trafficking pathway of CK was realized by triple cascade targeting including (1) CD44 targeting mediated by chondroitin sulfate, (2) Golgi apparatus targeting mediated by the caveolin-mediated endocytic pathway, and (3) endoplasmic reticulum (ER) targeting mediated by coat protein I (COP I) vesicles. CK was adsorbed on the complex of cationic liposomes (Lip) encapsulating siRNA targeting autophagy-related gene 7 (siATG7) to afford Lip/siATG7/CK. Lip/siATG7/CK functions as a drifting boat that follows the CD44-Golgi-ER flow and travels downstream to its destination (ER), bypassing the lysosomal degradation pathway and endowing HSCs with excellent RNAi efficiency. The efficient downregulation of ATG7 leads to an excellent antifibrotic effect both in vitro and in vivo.

Depriving Tumor Cells of Ways to Metastasize: Ferroptosis Nanotherapy Blocks Both Hematogenous Metastasis and Lymphatic Metastasis.

Blood and lymph are two main pathways of tumor metastasis; however, hematogenous metastasis and lymphatic metastasis are difficult to inhibit simultaneously. Ferroptosis provides a new breakthrough for metastasis inhibition, but how to effectively trigger ferroptosis in tumor cells remains a major challenge. Metastatic tumor cells are prone to ferroptosis in blood, while they may be protected from ferroptosis in lymph. In this study, a nanoplatform DA/RSL3 was constructed for the intracellular codelivery of the polyunsaturated arachidonic acid (AA) and the GPX4 inhibitor RSL3, which could not only induce ferroptosis but also alleviate ferroptosis resistance. As a result, DA/RSL3 effectively triggered ferroptosis in tumor cells, thereby impairing the ability of tumor cells to metastasize in both blood and lymph. Furthermore, a fucoidan blocking strategy was proposed to maximize the efficacy of DA/RSL3. Fu+DA/RSL3 showed excellent efficacy in 4T1 tumor-bearing mice. This ferroptosis nanotherapy is promising for metastatic cancer treatment.

Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data.

Investigators have recently introduced powerful methods for population genetic inference that rely on supervised machine learning from simulated data. Despite their performance advantages, these methods can fail when the simulated training data does not adequately resemble data from the real world. Here, we show that this "simulation mis-specification" problem can be framed as a "domain adaptation" problem, where a model learned from one data distribution is applied to a dataset drawn from a different distribution. By applying an established domain-adaptation technique based on a gradient reversal layer (GRL), originally introduced for image classification, we show that the effects of simulation mis-specification can be substantially mitigated. We focus our analysis on two state-of-the-art deep-learning population genetic methods-SIA, which infers positive selection from features of the ancestral recombination graph (ARG), and ReLERNN, which infers recombination rates from genotype matrices. In the case of SIA, the domain adaptive framework also compensates for ARG inference error. Using the domain-adaptive SIA (dadaSIA) model, we estimate improved selection coefficients at selected loci in the 1000 Genomes CEU population. We anticipate that domain adaptation will prove to be widely applicable in the growing use of supervised machine learning in population genetics.

Selective sweeps on different pigmentation genes mediate convergent evolution of island melanism in two incipient bird species.

Insular organisms often evolve predictable phenotypes, like flightlessness, extreme body sizes, or increased melanin deposition. The evolutionary forces and molecular targets mediating these patterns remain mostly unknown. Here we study the Chestnut-bellied Monarch (Monarcha castaneiventris) from the Solomon Islands, a complex of closely related subspecies in the early stages of speciation. On the large island of Makira M. c. megarhynchus has a chestnut belly, whereas on the small satellite islands of Ugi, and Santa Ana and Santa Catalina (SA/SC) M. c. ugiensis is entirely iridescent blue-black (i.e., melanic). Melanism has likely evolved twice, as the Ugi and SA/SC populations were established independently. To investigate the genetic basis of melanism on each island we generated whole genome sequence data from all three populations. Non-synonymous mutations at the MC1R pigmentation gene are associated with melanism on SA/SC, while ASIP, an antagonistic ligand of MC1R, is associated with melanism on Ugi. Both genes show evidence of selective sweeps in traditional summary statistics and statistics derived from the ancestral recombination graph (ARG). Using the ARG in combination with machine learning, we inferred selection strength, timing of onset and allele frequency trajectories. MC1R shows evidence of a recent, strong, soft selective sweep. The region including ASIP shows more complex signatures; however, we find evidence for sweeps in mutations near ASIP, which are comparatively older than those on MC1R and have been under relatively strong selection. Overall, our study shows convergent melanism results from selective sweeps at independent molecular targets, evolving in taxa where coloration likely mediates reproductive isolation with the neighboring chestnut-bellied subspecies.

A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph.

Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep-learning framework, we developed a novel method to detect and quantify positive selection: Selection Inference using the Ancestral recombination graph (SIA). Built on a Long Short-Term Memory (LSTM) architecture, a particular type of a Recurrent Neural Network (RNN), SIA can be trained to explicitly infer a full range of selection coefficients, as well as the allele frequency trajectory and time of selection onset. We benchmarked SIA extensively on simulations under a European human demographic model, and found that it performs as well or better as some of the best available methods, including state-of-the-art machine-learning and ARG-based methods. In addition, we used SIA to estimate selection coefficients at several loci associated with human phenotypes of interest. SIA detected novel signals of selection particular to the European (CEU) population at the MC1R and ABCC11 loci. In addition, it recapitulated signals of selection at the LCT locus and several pigmentation-related genes. Finally, we reanalyzed polymorphism data of a collection of recently radiated southern capuchino seedeater taxa in the genus Sporophila to quantify the strength of selection and improved the power of our previous methods to detect partial soft sweeps. Overall, SIA uses deep learning to leverage the ARG and thereby provides new insight into how selective sweeps shape genomic diversity.

Circadian immunity, sunrise time and the seasonality of respiratory infections.

The innate and adaptive immune response are regulated by biological clocks, and circulating lymphocytes are lowest at sunrise. Accordingly, severity of disease in mouse models is highly dependent on the time of day of viral infection. Here, we explore whether circadian immunity contributes significantly to seasonality of respiratory viruses, including influenza and SARS-CoV-2. Susceptibility-Infection-Recovery-Susceptibility (SIRS) models of influenza and SIRS-derived models of COVID-19 suggest that local sunrise time is a better predictor of the basic reproductive number (R0) than climate, even when day length is taken into account. Moreover, these models predict a window of susceptibility when local sunrise time corresponds to the morning commute and contact rate is expected to be high. Counterfactual modeling suggests that retaining daylight savings time in the fall would reduce the length of this window, and substantially reduce seasonal waves of respiratory infections.

Cross-species hybridization and the origin of North African date palms.

Date palm (Phoenix dactylifera L.) is a major fruit crop of arid regions that were domesticated ∼7,000 y ago in the Near or Middle East. This species is cultivated widely in the Middle East and North Africa, and previous population genetic studies have shown genetic differentiation between these regions. We investigated the evolutionary history of P. dactylifera and its wild relatives by resequencing the genomes of date palm varieties and five of its closest relatives. Our results indicate that the North African population has mixed ancestry with components from Middle Eastern P. dactylifera and Phoenix theophrasti, a wild relative endemic to the Eastern Mediterranean. Introgressive hybridization is supported by tests of admixture, reduced subdivision between North African date palm and P. theophrasti, sharing of haplotypes in introgressed regions, and a population model that incorporates gene flow between these populations. Analysis of ancestry proportions indicates that as much as 18% of the genome of North African varieties can be traced to P. theophrasti and a large percentage of loci in this population are segregating for single-nucleotide polymorphisms (SNPs) that are fixed in P. theophrasti and absent from date palm in the Middle East. We present a survey of Phoenix remains in the archaeobotanical record which supports a late arrival of date palm to North Africa. Our results suggest that hybridization with P. theophrasti was of central importance in the diversification history of the cultivated date palm.