Deep Reactive Ion Research Articles

Expeditious and environmentally benign synthesis of imidazo[4,5,1-ij]quinolines via sequential Povarov reaction/reductive cyclization.

In this contribution, a novel, simple, diastereoselective and environmentally benign two-step diversity-oriented synthesis of imidazo[4,5,1-ij]quinolines is described for the first time. The synthesis of the target compounds involves a deep eutectic solvent-mediated one-pot Povarov reaction leading to the obtention of 8-nitrotetrahydroquinolines, followed by a microwave-assisted reductive cyclocondensation employing different aromatic and aliphatic aldehydes. The target compounds were obtained in up to 70% overall yield starting from commercially available o-nitroanilines, natural phenylpropanoids (trans-anethole and trans-isoeugenol) and aromatic or aliphatic aldehydes. The eutectic solvent employed in the first step was reused in four runs without observing a drastic decrease in catalytic activity, and sodium dithionite showed to be an efficient and green reducing agent for the second step. This methodology provides significant advantages in terms of synthetic and green chemistry such as mild reaction conditions, short reaction time, energy-efficiency, simple work-up procedure, low cost, scalability and utilization of renewable substrates and a reusable solvent.

Highly selective production of 2-methylnaphthalene by CO2 hydrogenation and naphthalene alkylation

Many researchers have extensively studied the alkylation of naphthalene with methanol for the preparation of high-value-added chemicals, especially methylnaphthalenes (MNs). Studies indicated that methanol generates a large number of methanol-to-olefin by-products on zeolites, resulting in low selectivity of the target product and poor stability of the catalyst, limiting its application in the industrial field. In this study, CO2 hydrogenation was coupled with naphthalene alkylation, and the H3CO* active intermediate generated in situ was alkylated with naphthalene by using metal oxide-zeolite (OX-ZEO) bifunctional catalysts, which exhibited the ‘slow release’ phenomenon of the methylation reagent. It improved naphthalene conversion, suppressed deep alkylation and coke deposition side reactions and kept the catalyst stable for up to 500 h. A high-density and medium-strength Brønsted acid center facilitated the efficient migration of H3CO* active intermediates with the desorption of 2-methylnaphthalene (2-MN), which was particularly important for improving naphthalene conversion and 2-MN selectivity. The in situ IR showed that more Brønsted acid sites of Ga-S-1 effectively pulled the migration process of H3CO* species to the zeolitic acid center and increased the efficiency of C-C coupling compared to ZSM-5, which promoted the alkylation of H3CO* species with naphthalene. The results of chemisorption experiments showed that the medium-strength Brønsted acid site, which promoted the desorption of 2-MN, inhibited the deep alkylation reaction. The 2,6-dTBPy-IR results showed that the presence of fewer Brønsted centers on the outer surface of Ga-S-1 further optimized the product distribution and enhanced the selectivity of 2-MN in MNs to more than 90 %. Under optimal conditions, 80.1 % selectivity for MNs was obtained via ZnZrOx&Ga-S-1 bifunctional catalysts, with 97.4 % selectivity for 2-MN in MNs.

Comparative Study of PtM (M=Cu, Zn, Ga, Mn, Fe, In, Ce) Bimetals on Zincosilicate for Propane Dehydrogenation Reaction.

Silicoaluminate zeolites have relatively strong Brönsted (B) acid properties that can easily lead to deep cracking reactions, making them less favourable as carriers for propane dehydrogenation. Here, we utilise zincosilicate zeolite with less B-acid produced by the introduction of the heteroatom Zn into the framework as a carrier, followed by simultaneous ion exchange (IE) of M monometallic or PtM bimetallic (M=Cu, Zn and Ga, etc.). The optimized PtZn/Zn-4 exhibits a superior propane dehydrogenation performance over PtCu/Zn-4 and PtGa/Zn-4, which can achieve a propane conversion of about 30 % in a pure propane atmosphere at 550 °C and can be operated for at least 168 h without significant deactivation. Characterization techniques such as spherical aberration corrected transmission electron microscopy, in situ X-ray photoelectron spectroscopy, and in situ diffuse reflectance infrared fourier transform spectroscopy with different gas adsorptions are used to investigate these PtM@zeolite catalysts in order to deepen the understanding of acid site identification, promoter effect and catalysis.

Uncertainty Qualification for Deep Learning-Based Elementary Reaction Property Prediction.

The prediction of the thermodynamic and kinetic properties of elementary reactions has shown rapid improvement due to the implementation of deep learning (DL) methods. While various studies have reported the success in predicting reaction properties, the quantification of prediction uncertainty has seldom been investigated, thus compromising the confidence in using these predicted properties in practical applications. Here, we integrated graph convolutional neural networks (GCNN) with three uncertainty prediction techniques, including deep ensemble, Monte Carlo (MC)-dropout, and evidential learning, to provide insights into the uncertainty quantification and utility. The deep ensemble model outperforms others in accuracy and shows the highest reliability in estimating prediction uncertainty across all elementary reaction property data sets. We also verified that the deep ensemble model showed a satisfactory capability in recognizing epistemic and aleatoric uncertainties. Additionally, we adopted a Monte Carlo Tree Search method for extracting the explainable reaction substructures, providing a chemical explanation for DL predicted properties and corresponding uncertainties. Finally, to demonstrate the utility of uncertainty qualification in practical applications, we performed an uncertainty-guided calibration of the DL-constructed kinetic model, which achieved a 25% higher hit ratio in identifying dominant reaction pathways compared to that of the calibration without uncertainty guidance.

Microscopic Insights into Metallization of Diamond with Transition Metals

AbstractMetallization of diamond with transition metals (TMs) has attracted much attention as they play an important role in the development of precision machining, high‐end thermal management, especially for electronic devices. However, it is found that the solid‐state reaction does not stop at the interface, potentially hampering interface engineering. To unleash the full potential of diamond devices, deeper reactions are investigated in monocrystalline intrinsic diamond metallized with Ti/Pt/Au ohmic contacts combing experiments and theoretical calculations. Apart from exhibiting TiC, graphite, TMs, and diamond nanocrystallites at the interface, a unique ion‐implanted‐like multilayered structure is observed at micrometer depth. TM atoms penetrate through diamond produce a buried amorphous carbon layer under the damaged diamond lattice. In addition, these theoretical calculations reveal that the structural amorphization is catalyzed by the incorporation of TM atoms through vacancy‐mediated diffusion, which induces the closure of the bandgap of the diamond. This facilitates carriers crossing the bandgap via impurity band conduction and promotes the formation of the ohmic contact. This study provides critical insight into the microscopic mechanisms of the metallization between diamonds and TMs and could facilitate the development of diamond‐based electronic devices with a tailored electronic property.

Insights into the Effect of a Microwave Field on the Properties of Modified γ-Alumina: A DFT Study

γ-Alumina is often used as a support for hydrodesulfurization catalysts due to its excellent performance. During the catalytic reaction, the strong surface acidity of γ-alumina can induce a strong interaction between the active phase and the support. The reaction activity of the catalyst can be affected by changing the present mode of the active phase on the surface of the support. The (110) crystal plane, acting as the strongest acidity plane of γ-alumina, was selected for modification. The supports modified with boron and phosphorus were successfully constructed, and the acid strengths were quantified by simulating the adsorption of the relevant probe molecules: pyridine in correlation with surface electronic properties via density functional theory. The surface adsorption energy calculation shows that the boron-modified surface is able to moderately reduce the adsorption capacity of alumina, while that of the surface modified by phosphorus is found to be enhanced over the sites of a tetrahedral coordination structure; however, at the other unsaturated Al sites, this is obviously reduced. The results of introducing electric fields imply that applying horizontal electric fields changes the surface acidity of alumina under the premise of a stable structure. With the enhancement of the horizontal electric fields, the adsorption capacity of tetra-coordination sites on the original surface gradually decreases, while those of the others gradually increases. However, for the boron-modified surface, introducing horizontal electric fields can reduce the adsorption capacity of all sites. Hence, microwave-electric-field-assisted modification of B further reduces the surface acidity of alumina, making it beneficial for deep hydrodesulfurization reactions.

Cortinarius steglichii: a taxonomical and chemical novelty from Chile

The new species Cortinarius steglichii is described from Chilean Nothofagus forest based on morphological and microscopical attributes, molecular phylogeny, and chemical analysis of secondary metabolites. C. steglichii is characterized by abundant, long, ramified cystidia on the lamellar edges and stipe apex, further by a deep violet color reaction after treatment with KOH. As responsible secondary metabolite for the cytoplasmatic color reaction of cystidia and some hyphae, the new diterpenoid steglichon (1) could be recognized, showing also remarkable antibacterial and anticancer activity. Phylogenetic analyses (ITS, LSU, RPB1) confirm the close relationship to species of the Cortinarius dulciolens group.

Study on the Synergistic Effect of SiO2 and H2O on Oxidative Coupling of Methane over Mn-Na2WO4/SiO2 Catalyst.

Mn-Na2WO4-based catalysts with different supports were prepared using the incipient wetness impregnation method and evaluated for their oxidative coupling of methane (OCM) reaction performance. The results demonstrated that the SiO2- supported catalyst exhibited the best catalytic performance, and the introduction of H2O further enhanced its activity. Under the conditions of a feed gas mixture of CH4/O2/H2O = 6:1:24 at 800 °C and atmospheric pressure, the CH4 conversion and C2+ selectivity over the Mn-Na2WO4/SiO2 catalyst increased from 28.4% and 77.4% (without H2O) to 33.2% and 84.9%, respectively. In contrast, the catalytic activity using TiO2 and MgO supports drastically declined. Characterizations using X-ray diffraction (XRD), in situ infrared spectroscopy (In-situ IR), X-ray photoelectron spectroscopy (XPS), hydrogen temperature-programmed reduction (H2-TPR), and oxygen temperature-programmed desorption (O2-TPD) revealed that the introduction of steam not only served as a diluent to decrease the partial pressures of the reactants CH4 and O2, thereby reducing deep oxidation reactions in the gas phase, but also interacted with surface oxygen species (O2 2-) and silica to form adjacent surface-bonded disilanol free radicals Si-OIH. This interaction facilitated the dehydrogenation and coupling of two methane molecules on the catalyst surface, resulting in the generation of C2+ products, significantly enhancing the catalytic activity and selectivity of OCM.

Penerapan Slow Deep Breathing Untuk Menurunkan Keletihan pada Pasien Chronic Kidney Disease: Studi Kasus

Fatigue is the most common physical symptom felt by chronic kidney failure patients. Manifestations of fatigue are the body's inability to carry out routine activities, lethargy, and feeling tired all the time. Non-pharmacological therapy that can reduce fatigue is slow deep breathing relaxation therapy. To determine the intervention for providing slow deep breathing relaxation therapy in chronic kidney failure patients with fatigue. This research design is a case study using nursing care. Mr. I is 64 years old suffering from chronic kidney failure stage v who experiences problems with nursing fatigue. The intervention provided is by providing pharmacological and non-pharmacological therapy, slow deep breathing relaxation therapy. This study used the Functional Assessment of Chronic Illness Therapy-Fatigue Scale instrument. Fatigue complaints decreased with an increase in fatigue score. Before being given therapy, the slow deep breathing reaction was at a score of 22.6 became 40.33 after being given therapy for 3 days. Slow deep breathing can be given as an alternative non-pharmacological therapy which has the effect of reducing fatigue. It is hoped that nursing students or nurses in inpatient rooms can provide this therapy as a non-pharmacological intervention for chronic kidney failure patients with fatigue.

Comparison of quantitative detection of BCR::ABL1 p210 transcript levels: a multicenter study

Objective: To assess the capability of seven reference medical laboratories to detect BCR::ABL1 p210 transcription levels and to compare the results among those laboratories. Methods: The interlaboratory comparison was carried out in two stages. The samples were prepared by the reference laboratory. The quantitative values of BCR::ABL1 p210 of the comparison samples covered 0.001%-0.01%, 0.01%-0.1%, 0.1%-1%, 1%-10% and>10% in each stage. Real-time quantitative PCR (RT-PCR) and dPCR (digital PCR) were used to examine the samples. The conversion factor (CF) was calculated and validated for each laboratory. Results: In the RT-PCR comparison, one laboratory was failed to detect BCR::ABL1 p210 in fourteen samples at the first stage. The results of the other six laboratories were qualified with the bias <±1.2 folds (-0.133-0.338) and 95% limits of agreement within ±5 folds (upper limit 0.147-0.785, lower limit -0.770--0.109), and the corresponding CF values were calculated and validated. In the dPCR comparison, one laboratory did not report results at the second stage. The results of the other six laboratories were qualified with the bias <±1.2 folds (-0.026-0.267) and 95% limits of agreement within±5 folds (upper limit 0.084-0.991, lower limit -0.669--0.135), and the corresponding CF values were calculated and validated. The samples with BCR::ABL1 p210 quantitative values of 0.01%-0.1%, 0.1%-1%, 1%-10% and >10% could be detected by both RT-PCR and qPCR. When the quantitative value of BCR::ABL1 p210 was 0.001%-0.01%, the detection rate of dPCR was higher than that of RT-PCR (85.56% vs. 68.00%). Conclusions: A good consistency is present among various laboratories. The quantitative value of BCR::ABL1 p210 is comparable among laboratories as shown by the CF value conversion. For quantitative detection of BCR::ABL1 p210 deep molecular reaction, dPCR has a higher positive detection rate and more advantages than RT-PCR. To ensure the accuracy and reproducibility of the BCR::ABL1 p210 test, it is imperative for every laboratory to enhance their daily quality control practices.

Mitochondrial respiratory complex I can be inhibited via bypassing the ubiquinone-accessing tunnel.

Mitochondrial NADH-ubiquinone oxidoreductase (complex I) couples electron transfer from NADH to ubiquinone with proton translocation in its membrane part. Structural studies have identified a long (~ 30 Å), narrow, tunnel-like cavity within the enzyme, through which ubiquinone may access a deep reaction site. Although various inhibitors are considered to block the ubiquinone reduction by occupying the tunnel's interior, this view is still debatable. We synthesized a phosphatidylcholine-quinazoline hybrid compound (PC-Qz1), in which a quinazoline-type toxophore was attached to the sn-2 acyl chain to prevent it from entering the tunnel. However, PC-Qz1 inhibited complex I and suppressed photoaffinity labeling by another quinazoline derivative, [125I]AzQ. This study provides further experimental evidence that is difficult to reconcile with the canonical ubiquinone-accessing tunnel model.

Unraveling the function of monolayer shell structure over Pt-based alloy catalysts in tuning ethane dehydrogenation reactivity and coking resistance

Ethane direct dehydrogenation (EDH) operated at high temperature easily leads to coke formation. The new core–shell catalysts with monolayer shell exhibited better catalytic performance, and the function of monolayer shell structure is of great importance. This work fully investigated catalytic performance of EDH over six types of core–shell Pt3M@Pt and Pt@Pt3M (M = Fe, Co, and Ni) catalysts with Pt3M and Pt monolayer shell under the experimental conditions. Here, density functional theory (DFT) calculations with kinetic Monte Carlo (kMC) simulations were employed. The results show that the metal M over Pt3M@Pt surface decreases coordination number of Pt-Pt and restricts deep dehydrogenation reactions. The Pt3M@Pt catalysts with d-band center close to Fermi level exhibit higher C2H4(g) formation activity than Pt@Pt3M catalysts at 873.15 K. H2(g) co-feeding significantly decreases the coverage of coking C*+CC* species. The screened Pt3Ni@Pt catalyst presents the highest C2H4(g) formation activity and 100 % selectivity at the H2(g) partial pressure of 0.1 bar and reaction temperature 873.15 K, which is superior to previously reported Pt, Pt3Sn, 1Pt3Sn@4Pt, Pd1-NDG, Pt2/Al2O3 and Rh/ZrO2 catalysts. H* adsorption energy is proposed as a descriptor to quickly evaluate C2H4(g) formation activity. This work provides a useful strategy for designing alkane dehydrogenation catalysts by tuning monolayer shell structure and coordination environment of active site.

Developing deep learning-based large-scale organic reaction classification model via sigma-profiles

Advanced technologies like deep learning have accelerated the discovery of novel chemical reactions, especially in the field of organic synthesis. With hundreds of thousands of reactions available for reference, one way to effectively leverage them is by classifying chemical reactions into different clusters based on their specific characteristics, which makes target-guided navigation in the vast chemical space possible. Although previous attempts that apply deep learning to reaction classification tasks have made substantial progress, developing a model with good interpretability as well as high accuracy for large-scale reaction classification tasks remains an open question. In this work, a deep learning-based model for a large-scale reaction classification task is first constructed by utilizing pre-trained BERT and autoencoder. Then, the model is trained under the open-source dataset USPTO_TPL which contains recorded reactions of up to 1000 different types. The multi-classification accuracy of the model on the testing dataset is 99.382%, showing its great potential for practical use. Besides, a reaction similarity map is presented to correlate the reactions in the USPTO_TPL dataset based on their sigma-profile-based statistical features. Finally, representative reactions from the testing dataset are provided to illustrate the model's effectiveness on the reaction classification task.

The role of Au and Pd in photocatalytic activation of molecular oxygen: A mechanistic study

Activation of molecular oxygen plays an important role in photocatalytic selective oxidation reactions and complete degradation of organic pollutants. Noble metal cocatalysts are often employed to modulate the activation of oxygen to reach an optimum photocatalytic performance. However, the role of the metal nanoparticles (NPs) still remains unclear and deserves investigation for the design of efficient photocatalysts. Here, Au and Pd NPs are deposited on the TiO2 photocatalyst to study the promotional mechanism of O2 dissociation with the presence of glycerol under irradiation by near-ambient-pressure photoelectron spectroscopy and in-situ spectrometry approaches. While both Au and Pd NPs can dissociate O2 into activated oxygen species, a relatively strong bonds of dissociated oxygen atom is observed on Au NPs than on the Pd NPs. This results in a moderate reduction rate of O2 and eventually favors a mild oxidation reaction process for Au, and a rapid reduction rate of O2 and eventually a deep oxidation reaction process for Pd, respectively.

Deep-reactive ion etching of silicon nanowire arrays at cryogenic temperatures

The pursuit of sculpting materials at increasingly smaller and deeper scales remains a persistent subject in the field of micro- and nanofabrication. Anisotropic deep-reactive ion etching of silicon at cryogenic temperatures (cryo-DRIE) was investigated for fabricating arrays of vertically aligned Si nanowires (NWs) of a large range of dimensions from micrometers down to 30 nm in diameter, combined with commonly used wafer-scale lithography techniques based on optical, electron-beam, nanoimprint, and nanosphere/colloidal masking. Large selectivity of ∼100 to 120 and almost 700 was found with resists and chromium hard masks, respectively. This remarkable selectivity enables the successful transfer of patterned geometries while preserving spatial resolution to a significant extent. Depending on the requirements by applications, various shapes, profiles, and aspect ratios were achieved by varying process parameters synchronously or asynchronously. High aspect ratios of up to 100 comparable to the best result by metal-assisted wet-chemical etching and sub-μm trenches by DRIE were obtained with NW diameter of 200 nm, at an etch rate of ∼4 μm/min without being collapsed. At the same time, low surface roughness values were maintained on the NW top, sidewall, and bottom surface of ∼0.3, ∼13, and ∼2 nm, respectively, as well as high pattern fidelity and integrity, which were measured using angle-resolved Fourier microscopy, combined atomic force, and scanning electron microscopy on selected NWs. This work establishes the foundation in the controllable development of Si nanoarchitectures, especially at sub-100 nm structures, for energy-harvesting and storage, damage-free optoelectronics, quantum, photovoltaics, and biomedical devices.

Study on coupled heat transfer of pyrolytic kerosene and supercritical CO2 in zigzag-type PCHE used for hypersonic vehicle power generation system

Supercritical CO2 closed cycle is an effective solution to the power generation challenge of hypersonic vehicles. The coupled heat transfer between aviation kerosene and supercritical CO2 within the zigzag-type printed circuit heat exchanger (PCHE) is the core thermal process of the whole cycle. Starting from the enhancement of the cycle's power generation, this study analyzes the influence mechanism of structural parameters on the heat transfer process through numerical simulation. The research indicates that a deep pyrolysis reaction of aviation kerosene, with the chemical heat absorption rate accounting for over 65 % of the total heat transfer rate. Due to the significant nonlinearity introduced by pyrolysis, traditional enhanced heat transfer design methods may even weaken its heat exchange. Unlike most studies, this type of PCEH achieves optimal heat transfer performance with a larger channel diameter and a smaller bend angle. Additionally, the increase in diameter and the decrease in bend angle enhance the power capacity of kerosene and its pyrolysis products, significantly improving the efficiency of thermal-electric conversion for the cycle. Finally, the study has determined a diameter of 2.4 mm and a bend angle of 30° constitute a set of structural parameters most suitable for the PCHE.

Exploring First-Year Students’ Experience with Cadaver-Based Learning: A Qualitative Study

Background: Students usually experience deep emotional reactions and mixed feelings during the first encounter with the cadaver. These affective and emotional aspects were the important elements in forming the professional and medical integrity. This study aimed at exploring the first-year medical students' experience with cadaver-based learning in the Faculty of Medicine Pattimura University, Ambon, in the academic year of 2019/2020.Methods: Using a qualitative method, this study was conducted in December 2019. There were 42 informants: first-year students, senior students, and anatomy laboratory assistants. Data were collected using focus group discussion (FGD) and in-depth interviews.Results: Before attending class in the anatomy laboratory, our study informants experienced mixed feelings, including feeling attracted, curious, tensed, worried, anxious and even afraid. When they were in the middle of the class session in the laboratory, there were some physical reactions such as nausea, dizziness, and eye and nose sore due to the scent of formaldehyde. Our informants admitted they were also anxious and afraid of their first encounter with the cadaver. Emotional reactions, however, declined after repeated exposures to the anatomy laboratory. After participating in anatomy class, the informants mentioned that learning with cadavers increased their interest in anatomy.Conclusion: Overall, informants had a thrilling experience in the cadaver-based learning inside the anatomy laboratorium. Efforts to introduce the learning session early, even before the class starts, will be beneficial to prepare the students, in addition to the company of lecturers and anatomy laboratory assistants during class.

CircALMS1 Alleviates Pulmonary Microvascular Endothelial Cell Dysfunction in Pulmonary Hypertension.

Circular RNAs can serve as regulators influencing the development of pulmonary hypertension (PH). However, their function in pulmonary vascular intimal injury remains undefined. Thus, we aimed to identify specifically expressed circular RNAs in pulmonary microvascular endothelial cells (PMECs) under hypoxia and PH. Deep RNA sequencing and quantitative real-time polymerase chain reaction revealed that circALMS1 (circular RNA Alstrom syndrome protein 1) was reduced in human PMECs under hypoxia (P<0.0001). Molecular biology and histopathology experiments were used to elucidate the roles of circALMS1 in regulating PMEC dysfunction among patients with PH. The circALMS1 expression was decreased in the plasma of patients with PH (P=0.0315). Patients with lower circALMS1 levels had higher risk of death (P=0.0006). Moreover, the circALMS1 overexpression of adeno-associated viruses improved right ventricular function and reduced pulmonary vascular remodeling in monocrotaline-PH and sugen/hypoxia-PH rats (P<0.05). Furthermore, circALMS1 overexpression promoted apoptosis and inhibited PMEC proliferation and migration under hypoxia by directly downregulating miR-17-3p (P<0.05). Dual luciferase assay confirmed the direct binding of circALMS1 to miR-17-3p and miR-17-3p binding to its target gene YT521-B homology domain-containing family protein 2 (YTHDF2) (P<0.05). The YTHDF2 levels were also downregulated in hypoxic PMECs (P<0.01). The small interfering RNA YTHDF2 reversed the effects of miR-17-3p inhibitors on PMEC proliferation, migration, and apoptosis. Finally, the results indicated that, although YTHDF2, as an N(6)-methyladenosine reader protein, contributes to the degradation of many circular RNAs, it could not regulate the circALMS1 levels in PMECs (P=0.9721). Our study sheds new light on circALMS1-regulated dysfunction of PMECs by the miR-17-3p/YTHDF2 pathway under hypoxia and provides insights into the underlying pathogenesis of PH.

Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry.

Suzuki cross-coupling reactions are considered a valuable tool for constructing carbon-carbon bonds in small molecule drug discovery. However, the synthesis of chemical matter often represents a time-consuming and labour-intensive bottleneck. We demonstrate how machine learning methods trained on high-throughput experimentation (HTE) data can be leveraged to enable fast reaction condition selection for novel coupling partners. We show that the trained models support chemists in determining suitable catalyst-solvent-base combinations for individual transformations including an evaluation of the need for HTE screening. We introduce an algorithm for designing 96-well plates optimized towards reaction yields and discuss the model performance of zero- and few-shot machine learning. The best-performing machine learning model achieved a three-category classification accuracy of 76.3% (±0.2%) and an F 1-score for a binary classification of 79.1% (±0.9%). Validation on eight reactions revealed a receiver operating characteristic (ROC) curve (AUC) value of 0.82 (±0.07) for few-shot machine learning. On the other hand, zero-shot machine learning models achieved a mean ROC-AUC value of 0.63 (±0.16). This study positively advocates the application of few-shot machine learning-guided reaction condition selection for HTE campaigns in medicinal chemistry and highlights practical applications as well as challenges associated with zero-shot machine learning.

Examination of histopathological findings in scabies cases: a retrospective analysis of five years of experience.

Scabies is a skin infestation that has been known for hundreds of years and is caused by the ectoparasitis of Sarcoptes scabiei. Scabies is a public health problem that can be seen in all age groups, races, genders, socioeconomic groups, and all societies. The aim of this study was to identify the prominent histopathological findings in scabies cases and to guide the clinical approach. Scabies cases, which were clinically diagnosed between January 2016 and December 2020 in our hospital, were retrospectively screened, and 3,027 cases were detected. As a result of the screening, a skin punch biopsy-histopathological examination was performed in only 62 cases that were included in the study. Cases without punch biopsy were excluded from the study. After a histopathological evaluation, the cases were divided into two groups: those diagnosed with scabies (n=9) and those diagnosed with nonscabies (n=53). Pathological findings of the groups were statistically compared using Fisher's exact Chi-square test. After microscopic examination, superficial and deep inflammatory reactions and the presence of lymphocytes were detected in 100% of cases. Furthermore, eosinophils and spongiosis were detected in 55% of cases. Histomorphological findings of the two groups were statistically examined and significant differences were found in spongiosis and eosinophil findings (p=0.019, p=0.025). There were no statistically significant differences in the other histopathological findings (parakeratosis, vesicle, bullae, intraepidermal pustule, vasculitis, dermal edema, superficial and deep inflammatory reaction, neutrophils, and plasma cells - p=0.259, p=0.266, p=0.083, p=0.683, p=0.557, p=0.066, p=0.980, p=0.290, and p=0.083, respectively). In histopathological examination of scabies biopsies, spongiosis and eosinophil findings may be useful in differential diagnosis. Pathological diagnosis can be a decisive stage for controlling scabies, which has epidemic potential, especially in crowded environments such as schools, dormitories, and refugee camps. For pathology science to play an active role in the diagnosis of scabies, more punch biopsies are needed from cases. Large-sample prospective clinical studies comparing histopathological findings and the severity of the disease may contribute to the literature.