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This review summarizes recent progress of transfer learning (TL) in discovering advanced molecules and materials, constructing TL-assisted frameworks and addressing challenges of TL.

Past epidemiological and experimental studies in rodents have demonstrated that chronic kidney disease (CKD) leads to cognitive impairment. However, the underlying mechanism requires further investigation. Herein, a mouse model of CKD was established using conventional 5/6 nephrectomy. We aimed to examine the relationship between CKD and cognitive impairment and elucidate the underlying mechanisms. Cognitive behavior was assessed using the Morris water maze, novel object recognition test, and fear conditioning test. Further experiments were also conducted to investigate the underlying molecular mechanisms. Our clinical data revealed a decrease in cognitive function among patients with CKD, accompanied by elevated plasma levels of pro‐inflammatory cytokines. A positive correlation between cytokine concentrations and serum creatinine levels, as well as a significant positive correlation with cognitive dysfunction, were observed. Correlation analyzes demonstrated that hippocampal cytokine levels were positively correlated with serum creatinine levels and cognitive dysfunction in CKD model mice. Furthermore, 20 mg/mL interleukin‐6 (IL‐6) significantly decreased HT22 cell activity in vitro. Further, HT22 cells treated with IL‐6 showed increased expression levels of toll‐like receptor 4 (TLR4) and myeloid differentiation primary response gene 88 (MyD88), thereby inducing the nuclear factor kappa‐B p65 inflammatory pathway and mitochondria‐dependent apoptosis. The CKD mouse model showed increased expression of TLR4 and cytokines in the hippocampus. TLR4knockdown antagonized the IL‐6‐mediated pro‐inflammatory and pro‐apoptotic effects in HT22 cells. TLR4knockdown in the CKD model mice decreased hippocampal inflammation and increased the number of neuron dendrites, thus ameliorated cognitive impairment. These results suggest that IL‐6 triggers TLR4 activation to induce neuroinflammation and neurodegeneration in CKD, ultimately culminate in cognitive impairment. Diagrammatic representation of the role of IL‐6/TLR4 between CKD and cognitive impairment. CKD patients suffer fromimpaired renal function, accompanied by inflammatory reactions in the body, and simultaneous increased levels of inflammatory factors in the brain, especially IL‐6. This triggers overactivation of the TLR4/MyD88 pathway, increasing the expression of p65 and CL‐Casp3, and thus promoting neuroinflammation and neurodegeneration leading to cognitive dysfunction. CKD, chronic kidney disease; IL, interleukin; TLR4, Toll‐like receptor 4. Chronic kidney disease (CKD) leads to cognitive impairment, with elevated levels of pro‐inflammatory cytokines such as interleukin‐6 (IL‐6) playing a significant role.IL‐6 was shown to activate the toll‐like receptor 4 (TLR4)/myeloid differentiation factor 88/nuclear factor kappa B inflammatory pathway, resulting in increased apoptosis and inflammation in neurons.TLR4knockdown in a CKD mouse model reduced neuroinflammation, and improved cognitive function. Chronic kidney disease (CKD) leads to cognitive impairment, with elevated levels of pro‐inflammatory cytokines such as interleukin‐6 (IL‐6) playing a significant role. IL‐6 was shown to activate the toll‐like receptor 4 (TLR4)/myeloid differentiation factor 88/nuclear factor kappa B inflammatory pathway, resulting in increased apoptosis and inflammation in neurons. TLR4knockdown in a CKD mouse model reduced neuroinflammation, and improved cognitive function.

Iodine is an essential element that is used to make thyroid hormones. However, people usually ignore their iodine nutrition level, thus leading to a series of thyroid diseases, particularly in areas where medical resources are scarce. Thus, development of a portable, economical, and simple method for the detection of urinary iodine is of significant importance. Herein, a solid-phase fluorescence filter effect (SPFFE) induced by iodine was used to develop an SPFFE-based point-of-care testing (POCT) platform for the detection of urinary iodine by coupling with headspace sample introduction. This method can not only alleviate the matrix interference that occurred in the conventional inner filter effect (IFE) but also achieve high sensitivity. Furthermore, the urinary iodine (UI) POCT platform was developed through the integration of a sample pretreatment and fluorescence readout. This whole system costs less than US $20 and provides accurate temperature control and a portable fluorescence reading within 15–20 min. Compared to the traditional IFE-based assay, the SPFFE-based POCT platform allows the selective detection of iodine as low as 10 nM and has a linear range of 0.05–4 μM. In addition, it provides notable visualization from blue-violet to orange-red in the presence of iodine, which tends to indicate the iodine nutritional status of the human body. Eventually, the clinical applicability and feasibility of the UIPOCT platform as an early diagnostic test kit were confirmed by determining the iodine in urine samples from children and pregnant women.

Excessive consumption of Δ9-tetrahydrocannabinol (THC) severely endangers human health and has raised public safety concerns. However, its quantification by readily rapid tools with simplicity and low cost is still challenging. Herein, we found that a G-rich THC aptamer (THC1.2) can tightly bind to thioflavin T (ThT) with strong fluorescence, which would be specifically quenched in the presence of THC. Based on that, a label-free ratiometric fluorescent sensor for the sensing of THC and its metabolite (THC-COOH) based on THC1.2/ThT as a color emitter and red CdTe quantum dots as reference fluorescence was constructed. Notably, a transition of the fluorescent color of the ratiometric probe from green to red can be instantly observed upon the increased concentration of THC and THC-COOH. Furthermore, a portable smartphone-based fluorescence device integrated with a self-programmed Python program was fabricated and used to accomplish on-site monitoring of THC and THC-COOH within 5 min. Under optimized conditions, this ratiometric fluorescent sensor allowed for an instant response toward THC and its metabolite with considerable limits of detection of 97 and 254 nM, respectively. The established sensor has been successfully applied to urine and saliva samples and exhibited satisfactory recoveries (88–116%). This ratiometric fluorescent sensor can be used for the simultaneous detection of THC and THC-COOH with the advantages of rapidness, low cost, ease of operation, and portability, providing a promising strategy for on-site detection and facilitating law enforcement regulation and roadside control of THC.

The urgent need for field detection of uranium in seawater is 2-fold: to provide prompt guidance for uranium extraction and to prevent human exposure to nuclear radiation. However, current methods for this purpose are largely hindered by bulky instrumentation, high costs of developed materials, and severe matrix interferences, which limit their further application in the field. Herein, we demonstrated a portable and label-free strategy for the field detection of uranyl in seawater based on the efficient photocleavage of DNA. Further experiments confirmed the generation of ultraviolet (UV) light-induced reactive oxygen species (ROS), such as O2•–and •OH, which fragmented oligomeric DNA in the presence of uranyl and UV light. Detailed studies showed that DNA significantly enhances uranyl absorption in the UV–visible region, leading to the generation of more ROS. A fluorescence system for the selective detection of uranyl in seawater was established by immobilizing two complementary oligonucleotides with the fluorescent dye SYBR Green I. The strategy of UV-induced photocleavage offers high selectivity, excellent interference immunity, and high sensitivity for uranyl, with a detection limit of 6.8 nM. Additionally, the fluorescence can be visually detected using a 3D-printed miniaturized device integrated with a smartphone. This method has been successfully applied to the on-site detection of uranyl in seawater in 18 Chinese coastal cities and along the coast of Hainan Island within 3 min for a single sample. The sample testing and field analysis results indicate that this strategy has promising potential for real-time monitoring of trace uranyl in China’s coastal waters. It is expected to be utilized for the rapid assessment of nuclear contamination and nuclear engineering construction.

To meet the needs of food safety for simple, rapid, and low-cost analytical methods, a portable device based on a point discharge microplasma optical emission spectrometer (μPD-OES) was combined with machine learning to enable on-site food freshness evaluation and detection of adulteration. The device was integrated with two modular injection units (i.e., headspace solid-phase microextraction and headspace purge) for the examination of various samples. Aromas from meat and coffee were first introduced to the portable device. The aroma molecules were excited to specific atomic and molecular fragments at excited states by room temperature and atmospheric pressure microplasma due to their different atoms and molecular structures. Subsequently, different aromatic molecules obtained their own specific molecular and atomic emission spectra. With the help of machine learning, the portable device was successfully applied to the assessment of meat freshness with accuracies of 96.0, 98.7, and 94.7% for beef, pork, and chicken meat, respectively, through optical emission patterns of the aroma at different storage times. Furthermore, the developed procedures can identify beef samples containing different amounts of duck meat with an accuracy of 99.5% and classify two coffee species without errors, demonstrating the great potential of their application in the discrimination of food adulteration. The combination of machine learning and μPD-OES provides a simple, portable, and cost-effective strategy for food aroma analysis, potentially addressing field monitoring of food safety.

Fin field-effect transistors (FinFETs) have been widely used in electronic devices on account of their excellent performance, but this new type of device is facing many challenges because of size constraints. Two-dimensional (2D) materials with a layer structure can meet the required thickness of FinFETs and provide ideal carrier transport performance. In this work, we used 2D tellurene as the parent material and modified it with doping techniques to improve electronic device performance. High-performance FinFET devices were prepared with 23 systems screened from 385 doping systems by a combination of first-principle calculations and a machine-learning (ML) model. Moreover, theoretical calculations demonstrated that 1S1@Te and 2S2@Te have high carrier mobility and stability with an electron mobility and a hole mobility of 6.211 × 104cm2V−1S−1and 1.349 × 104cm2V−1S−1, respectively. This work can provide a reference for subsequent experiments and advance the development of functional materials by using an ML-assisted design paradigm.

The exceptional properties of two-dimensional hybrid organic–inorganic lead-halide perovskites (2D HOIPs) have led to a rapid increase in the number of low-dimensional materials for optoelectronic engineering and solar energy conversion. The flexibility and controllability of 2D HOIPs create a vast structural space, which presents an urgent issue to effectively explore 2D HOIPs with better performance for practical applications. However, the traditional RP-DJ classification method falls short in describing the influence of structure on the electronic properties of 2D HOIPs. To overcome this limitation, we employed inorganic structure factors (SF) as a classification descriptor, which considers the influence of inorganic layer distortion of 2D HOIPs. And we investigated the relationship between SF, other physicochemical features, and band gaps of 2D HOIPs. By using this structural descriptor as a feature for a machine learning model, a database of 304920 2D HOIPs and their structural and electronic properties was generated. A large number of previously neglected 2D HOIPs were discovered. With the establishment of this database, experimental data and machine learning methods were combined to develop a 2D HOIPs exploration platform. This platform integrates searching, download, analysis, and online prediction, providing a useful tool for the further discovery of 2D HOIPs.

Ensemble classification algorithms and oversampling techniques boost the development of a new family of high-performance heteroatom SACs for hydrogen evolution reaction.