Lithium-doped ZrO2 nanoparticles for SERS-based norfloxacin drug detection.

Surface-enhanced Raman scattering (SERS) spectroscopy is a highly specific and ultrasensitive analytical technique; thus, it is an ideal candidate for therapeutic drug monitoring. However, SERS measurements of drugs in a sample are inevitably affected by the environment. In this study, we synthesized ZrO2 nanoparticles (NPs) doped with the first group of elements (Li, Na, and K) in the main block and evaluated their SERS performance. The results showed that Li-ion doping could significantly enhance the SERS effect, and the degree of enhancement depended on the type and concentration of the doped ions. Compared with the highly stable ZrO2, Li ion-doped ZrO2 (Li-ZrO2) exhibited a significant increase in SERS activity. In particular, 1 % Li-ZrO2 NPs exhibited excellent SERS enhancement with an enhancement factor (EF) of 2.60 × 104, which was attributed to the decreased band gap and improved the charge transfer (CT) process after Li ion doping. The adsorption capacity of the Li-ZrO2 NPs for norfloxacin (NOR) molecules was gradually saturated with time. In addition, both acidic and alkaline conditions were unfavorable for NOR detection by the substrate. The SERS intensity exhibited a linear relationship within the NOR concentration range of 10-3-10-6 mol/L, and approximately 97.51 % of the active ingredients were detected, with a competitive detection limit of 10-6 mol/L. Furthermore, NOR detection is cost-effective and time-efficient, and the results of our study can aid in the research process and support practical applications. The proposed study provides a guidance for improving the SERS activity of semiconductors for sensing.

MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction.

Drug therapy remains the primary approach to treating tumours. Variability among cancer patients, including variations in genomic profiles, often results in divergent therapeutic responses to analogous anti-cancer drug treatments within the same cohort of cancer patients. Hence, predicting the drug response by analysing the genomic profile characteristics of individual patients holds significant research importance. With the notable progress in machine learning and deep learning, many effective methods have emerged for predicting drug responses utilizing features from both drugs and cell lines. However, these methods are inadequate in capturing a sufficient number of features inherent to drugs. Consequently, we propose a representational approach for drugs that incorporates three distinct types of features: the molecular graph, the SMILE strings, and the molecular fingerprints. In this study, a novel deep learning model, named MCMVDRP, is introduced for the prediction of cancer drug responses. In our proposed model, an amalgamation of these extracted features is performed, followed by the utilization of fully connected layers to predict the drug response based on the IC50 values. Experimental results demonstrate that the presented model outperforms current state-of-the-art models in performance.

scSwinFormer: A Transformer-Based Cell-Type Annotation Method for scRNA-Seq Data Using Smooth Gene Embedding and Global Features.

Single-cell omics techniques have made it possible to analyze individual cells in biological samples, providing us with a more detailed understanding of cellular heterogeneity and biological systems. Accurate identification of cell types is critical for single-cell RNA sequencing (scRNA-seq) analysis. However, scRNA-seq data are usually high dimensional and sparse, posing a great challenge to analyze scRNA-seq data. Existing cell-type annotation methods are either constrained in modeling scRNA-seq data or lack consideration of long-term dependencies of characterized genes. In this work, we developed a Transformer-based deep learning method, scSwinFormer, for the cell-type annotation of large-scale scRNA-seq data. Sequence modeling of scRNA-seq data is performed using the smooth gene embedding module, and then, the potential dependencies of genes are captured by the self-attention module. Subsequently, the global information inherent in scRNA-seq data is synthesized using the Cell Token, thereby facilitating accurate cell-type annotation. We evaluated the performance of our model against current state-of-the-art scRNA-seq cell-type annotation methods on multiple real data sets. ScSwinFormer outperforms the current state-of-the-art scRNA-seq cell-type annotation methods in both external and benchmark data set experiments.

Identification of a novel HLA-C allele, HLA-C*02:246.

HLA-C*02 246 has one nucleotide change from HLA-C*02:02:02:01 at nucleotide 523 changing Arginine to Cysteine at residue 151.

Alzheimer's disease early screening and staged detection with plasma proteome using machine learning and convolutional neural network.

Alzheimer's disease (AD) stands as the prevalent progressive neurodegenerative disease, precipitating cognitive impairment and even memory loss. Amyloid biomarkers have been extensively used in the diagnosis of AD. However, amyloid proteins offer limited information about the disease process and accurate diagnosis depends on the presence of a substantial accumulation of amyloid deposition which significantly impedes the early screening of AD. In this study, we have combined plasma proteomics with an ensemble learning model (CatBoost) to develop a cost-effective and non-invasive diagnostic method for AD. A longitudinal panel has been identified that can serve as reliable biomarkers across the entire progression of AD. Simultaneously, we have developed a neural network algorithm that utilizes plasma proteins to detect stages of Alzheimer's disease. Based on the developed longitudinal panel, the CatBoost model achieved an area under the operating curve of at least 0.90 in distinguishing mild cognitive impairment from cognitively normal. The neural network model was utilized for the detection of three stages of AD, and the results demonstrated that the neural network model exhibited an accuracy as high as 0.83, surpassing that of the traditional machine learning model.

Radionuclide-Labeled Antisilencing Function 1a Inhibitory Peptides for Tumor Identification and Individualized Therapy.

Immune checkpoint blockade (ICB) therapy is promising to revolutionize cancer regimens, but the low response rate and the lack of a suitable patient stratification method have impeded universal profit to cancer patients. Noninvasive positron emission tomography (PET) imaging in the whole body, upon coupling with specific biomarkers closely related to the immune response, could provide spatiotemporal information to prescribe cancer therapy. Herein, we demonstrate that antisilencing function 1a (ASF1a) could serve as a biomarker target to delineate tumor immune microenvironments by immune PET (iPET). The iPET radiotracer (68Ga-AP1) is designed to target ASF1a in tumors and predict immune response, and the signal intensity predicts anti-PD-1 (αPD-1) therapy response in a negative correlation manner. The ICB-resistant tumors with a high level of ASF1a as revealed by iPET (ASF1aHigh-iPET) are prescribed to be treated by either the combined 177Lu-labeled AP1 and αPD-1 or the standalone α particle-emitting 225Ac-labeled AP1, both achieving enhanced therapeutic efficacy and prolonged survival time. Our study not only replenishes the iPET arsenal for immune-related response evaluation by designing a reliable biomarker and a facile radiotracer but also provides optional therapeutic strategies for ICB-resistant tumors with versatile radionuclide-labeled AP1 peptides, which is promising for real-time clinical diagnosis and individualized therapy planning simultaneously.

Identification of the HLA-A*11:463 allele in a Chinese patient and his father.

HLA-A*11:463 has one nucleotide change from HLA-A*11:01:01:01 at nucleotide 508 changing Lysine (146) to Glutamine.

A Polymeric Hydrogel to Eliminate Programmed Death-Ligand 1 for Enhanced Tumor Radio-Immunotherapy.

Programmed death-ligand 1 (PD-L1) is a specialized shield on tumor cells that evades the immune system. Even inhibited by PD-L1 antibodies, a cycling process constantly transports PD-L1 from inside to outside of cells, facilitating the renewal and replenishment of PD-L1 on the cancer cell membrane. Herein, we develop a sodium alginate hydrogel consisting of elesclomol-Cu and galactose to induce persistent cuproptosis, leading to the reduction of PD-L1 for radio-immunotherapy of colon tumors. First, a prefabricated hydrogel is synthesized by immobilizing elesclomol onto a sodium alginate saccharide chain through the coordination with bivalent copper ions (Cu2+), followed by incorporation of galactose. After implantation into the tumors, this prefabricated hydrogel can be further cross-linked in the presence of physiological calcium ions (Ca2+), resulting in the formation of a hydrogel with controlled release of elesclomol-Cu2+ (ES-Cu) and galactose. The hydrogel effectively induces the oligomerization of DLAT and cuproptosis in colorectal cancer cells. Interestingly, radiation-induced PD-L1 upregulation is abrogated in the presence of the hydrogel, releasing ES-Cu and galactose. Consequently, the sensitization of tumor to radiotherapy and immunotherapy is significantly improved, further prolonging the survival of tumor-bearing mice in both local and metastatic tumors. Our study introduces an approach that combines cuproptosis with immunotherapy and radiotherapy.

Autologous hematopoietic recovery after allogeneic hematopoietic stem cell transplantation: A case-based review.

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is widely applied for the treatment of hematologic malignancies, but autologous hematopoietic recovery (AR) after allo-HSCT is rare clinically, especially after myeloablative conditioning (MAC). The mechanism of AR remains unclear so far, but the prognosis for most patients is relatively good. Second transplantation is preferred after disease relapse. Starting from a real-life clinical case scenario, herein we reviewed some of the crucial issues of AR in light of recent refinements, and discussed our patients based on the current evidence.

Identification of the novel HLA-C*01:02:86 allele in a Chinese individual.

HLA-C*01:02:86 has one synonymous nucleotide C > T change from HLA-C*01:02:01:01 at nucleotide 879 (residue 269 Proline).

Near-infrared upconversion multimodal nanoparticles for targeted radionuclide therapy of breast cancer lymphatic metastases.

The theranostics of lymph node metastasis has always been one of the major obstacles to defeating breast cancer and an important decisive factor in the prognosis of patients. Herein, we design NaGdF4:Yb,Tm@NaLuF4 upconversion nanoparticles with PEG and anti-HER2 monoclonal antibody (trastuzumab, Herceptin) (NP-mAb), the delivery of NP-mAb through the lymphatic system allows for effective targeting and accumulation in lymphatic metastasis. Combination of radionuclides 68Ga and 177Lu could be chelated by the bisphosphate groups of NP-mAb. The obtained nanoprobe (NP-mAb) and nanonuclear drug (68Ga-NP-mAb or 177Lu-NP-mAb) exhibited excellent stability and show high accumulation and prolong retention in the lymph node metastasis after intratumoral injection into the foot pad by near-infrared fluorescence (NIRF), single-photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging. Utilizing the ß-rays released by 177Lu, 177Lu-NP-mAb could not only decrease the incidence of lymph node metastasis, but also significantly decrease the volumes of lymph node metastasis. Additionally, 177Lu-NP-mAb induce no obvious toxicity to treated mice through blood routine, liver and kidney function assay. Therefore, nanoprobe and nanonuclear drug we designed could be acted as excellent theranostics agents for lymph node metastasis, providing potential alternatives diagnose and treatment option for lymph node metastasis.

Detection of an HLA-C*01 variant, HLA-C*01:212, in a Chinese individual.

HLA-C*01:212 differs from HLA-C*01:02:01:01 by two non-synonmous nucleotide changes at positions 368 and 379 in exon 3.

Identification of the novel allele HLA-B*13:157 by sequence-based typing.

HLA-B*13:157 has one nucleotide change from HLA-B*13:02:01:01 at nucleotide 323 changing Tyrosine to Phenylalanine at residue 84.

A novel HLA-C allele, HLA-C*15:244.

HLA-C*15:244 has one nucleotide change from HLA-C*15:05:01:01 at nucleotide 308 changing Arginine to Glutamine at residue 79.

Characterization of the novel allele HLA-B*35:251:02.

HLA-B*35:251:02 has one nucleotide change from HLA-B*35:22:01:01 at nucleotide 363 changing Serine to Arginine at residue 97.

Sequence-based typing identification of the novel allele HLA-B*40:482.

HLA-B*40:482 has one nucleotide change from HLA-B*40:06:01:01 at nucleotide 430 changing glycine to arginine at residue 120.

Recognition of the HLA-A*11:398 allele in a Chinese patient and his sister.

HLA-A*11:398 has one nonsynonymous nucleotide change from HLA-A*11:01:01:01 at nucleotide 709, changing Isoleucine 213 to Valine.

Identification of the novel HLA-A*02:981 allele by sequencing-based typing.

One nucleotide replacement at position 728 of HLA-A*02:07:01 results in a novel allele, HLA-A*02:981.

Recognization of the HLA-A*24:516 allele in a Chinese patient and his daughter.

HLA-A*24:516 has one nucleotide change from HLA-A*24:02:01:01 at nucleotide 194 where Alanine (41) is changed to Glycine.

Operando Raman spectroscopic evidence of electron-phonon interactions in NiO/TiO2 pn junction photodetectors.

The pn junctions significantly affect the responsivity of photodetectors (PDs). However, the enhancement mechanism of the pn junction is still unclear. Herein, operando Raman spectroscopy was employed to study PDs with NiO/TiO2 pn junctions composed of p-NiO nanoparticles (NPs) and n-TiO2 nanotube arrays (TNAs). The results suggest that the built-in potential field of the NiO/TiO2 interface decreases the charge transfer resistance and changes the vibrational frequency of the phonon modes of TiO2, which is attributed to the electron-phonon coupling effect. Operando Raman spectroscopy is proved to be a powerful tool for manufacturing highly responsive PDs.