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This study aimed to evaluate the suitability of alginate gels, specifically ferric-ion-cross-linked alginate (Fe-alginate) and calcium-ion-cross-linked alginate (Ca-alginate), as scaffolds for soil microbial attachment and biofilm formation in soil. Staining with crystal violet and observations with scanning electron microscopy showed that microorganisms formed biofilms on Fe-alginate surfaces in the soil. When the soil was incubated with Fe-alginate, microbial biomass, estimated by adenosine triphosphate content, increased not only in the Fe-alginate but also in the surrounding soil. The weight of Ca-alginate in the soil decreased with time owing to chemical dissolution. However, the weight of Fe-alginate in the soil did not decrease, likely because it was protected by the microbial biofilm that formed on its surface. These results demonstrate that the use of Fe-alginate, in contrast to Ca-alginate, as a scaffold may allow for more efficient use of soil microbial functions in agriculture and bioremediation., (© The Author(s) 2024. Published by Oxford University Press on behalf of Japan Society for Bioscience, Biotechnology, and Agrochemistry.)

fac -[Mn I (diimine)(CO) 3 (L)] 0/+ has attracted significant attention as a catalyst for the photocatalytic reduction of CO 2 . However, in such photocatalytic systems, the photoexcitation of Mn complexes and reaction intermediates induces their decomposition, which lowers the durability of these systems. In this study, we clarified the primary process whereby the Mn complex catalyst decomposes during the photocatalytic reaction. Based thereupon, we successfully constructed a highly durable photocatalytic system, of which the turnover number of formate (TON HCOO- ) exceeded 1700 when fac -[Mn I (bpy)(CO) 3 ((OC(O)OC 2 H 5 N(C 2 H 5 OH) 2 ) ( Mn-CO 2 -TEOA ) as the catalyst, [Os II (4,4'-dimethyl-bpy)(5,5'-dimethyl-bpy) 2 ] 2+ ( Os ) as the photosensitizer, and 1,3-dimethyl-2-phenyl-2,3-dihydro-1H-benzo[d]imidazole ( BIH ) as the reductant were used in conjunction with irradiation at λ ex ≥ 620 nm. In contrast, for the same photocatalytic system, irradiation at λ ex ≥ 480 nm lowered the TON HCOO- to less than 60. The significant difference in the durability of the photocatalytic system arises from the dependence of the Mn(0)-Mn(0) dimer [Mn 0 2 (bpy) 2 (CO) 6 ] ( Dim-Mn ), an intermediate produced during the photocatalytic reaction, on the wavelength of the irradiated light for its photoreactivity. That is, the irradiation of Dim-Mn at λ ex ≥ 620 nm selectively induces splitting of the Mn-Mn bond to produce [Mn 0 (bpy)(CO) 3 ] ( Mn • ) and, contrary to this, splitting of the Mn(0)-CO bonds and further decomposition processes are induced by irradiation at λ ex ≥ 480 nm.

Unraveling synaptic interactions between excitatory and inhibitory interneurons within rhythmic neural circuits, such as central pattern generation (CPG) circuits for rhythmic motor behaviors, is critical for deciphering circuit interactions and functional architecture, which is a major problem for understanding how neural circuits operate. Here we present a general method for extracting and separating patterns of inhibitory and excitatory synaptic conductances at high temporal resolution from single neuronal intracellular recordings in rhythmically active networks. These post-synaptic conductances reflect the combined synaptic inputs from the key interacting neuronal populations and can reveal the functional connectome of the active circuits. To illustrate the applicability of our analytic technique, we employ our method to infer the synaptic conductance profiles in identified rhythmically active interneurons within key microcircuits of the mammalian (mature rat) brainstem respiratory CPG and provide a perspective on how our approach can resolve the functional interactions and circuit organization of these interneuron populations. We demonstrate the versatility of our approach, which can be applied to any other rhythmic circuits where conditions allow for neuronal intracellular recordings.

Community-acquired pneumonia (CAP) is associated with high mortality rates and often results in prolonged hospital stays. The potential of machine learning to enhance prediction accuracy in this context is significant, yet clinicians often lack the programming skills required for effective data mining. This study aimed to assess the effectiveness of a low-code approach for assisting clinicians with data mining for mortality and length of stay (LOS) prediction in patients with CAP. A retrospective study was conducted using a low-code platform and the PyCaret library in Google Colab on data from patients with community-acquired pneumonia (CAP) admitted between January 2013 and December 2021 to two medical facilities. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) for mortality prediction and the R2 score for LOS prediction, with benchmarks set at AUC > 0.9 and R2 > 0.5. The Shapley Additive Explanations (SHAP) method was used for interpreting individual predictions. A total of 669 CAP patients were enrolled in the analysis.Fifteen models were evaluated for mortality prediction, and nineteen models were evaluated for LOS prediction utilizing the PyCaret library. The Light Gradient Boosting Machine model yielded the highest AUC (0.963) for mortality prediction. In predicting LOS, the Extratrees Regressor model achieved the highest R2 score of 0.585. Factors such as the severity of pneumonia and the Charlson Comorbidity Index (CCI) were significant factors influencing mortality. For the LOS, the CCI score, activities of daily living, and social support were significant predictors. The low-code approach enables medical professionals with limited technical expertise to effectively employ data science in their clinical decision-making process. This approach proved to be a valuable tool in the analysis of CAP patient data., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Introduction: The diagnostic tools of nucleic acid amplification tests and antigen tests have been extensively employed for the detection of Coronavirus disease 2019 (COVID-19). Although the reverse-transcriptase polymerase chain reaction (RT)-PCR test has high sensitivity and specificity, it is a time-consuming and labor-intensive process. On the other hand, antigen tests are simple and prompt, however, their low sensitivity and potential for false positives have been identified as limitations. In light of these factors, the development of novel tests that combine speed and clinical dependability is a promising prospect., Methods: Surface plasmon field-enhanced fluorescence spectroscopy (SPFS) excites chromophores by means of an enhanced electromagnetic field induced on a gold film surface. It enables the highly sensitive measurement of biomarkers in a short and simple 20-min window. In this study, a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) SPFS-based antigen test targeting the SARS-CoV-2 nucleocapsid protein was performed and evaluated in 25 patients with COVID-19 and 10 non-infected controls., Results: A positive correlation was observed between antigen levels determined by SPFS and RNA levels determined via RT-PCR. The sensitivity values were 100 %, 92 %, and 62.5 %; and the specificity values were 100 %, 90 %, and 100 %; for nasopharyngeal swabs, nasal swabs, and saliva specimens when the cutoff values were set to 65.1, 0.2, and 1.5 pg/mL, respectively. No clinically problematic cross-reactivity with analogous coronaviruses was observed., Conclusions: The SARS-CoV-2 SPFS antigen test showed excellent clinical diagnostic accuracy for nasopharyngeal and nasal swabs, with a rapid turnaround., (Copyright © 2024 Japanese Society of Chemotherapy, Japanese Association for Infectious Diseases, and Japanese Society for Infection Prevention and Control. Published by Elsevier Ltd. All rights reserved.)