Biomarker identification and risk assessment of cardiovascular disease based on untargeted metabolomics and machine learning.

Cardiovascular disease (CVD) is the leading cause of mortality, disability, and healthcare costs, with a significant impact on the elderly and contributing to premature deaths across various age groups, including those below age 70. Despite decades of transformative discoveries and clinical efforts, the challenges of diagnosis, prevention, and treatment of CVD persist on a massive scale. This study aimed to unravel potential CVD-associated biomarkers and establish a machine learning model for the risk assessment of CVD. Untargeted metabolic assay with ultra-high performance liquid chromatography-tandem mass spectrometry and routine clinical biochemistry test were undertaken on the fasting venous blood specimens from 57 subjects. Four relevant clinical traits and 164 CVD-associated metabolites were identified, especially those related to glycerophospholipid metabolism and biosynthesis of unsaturated fatty acids. The machine learning model achieved from an integrated biomarker panel of palmitic amide, oleic acid, 138-pos (the 138th detected metabolomic feature in positive ion mode), phosphatidylcholine, linoleic acid, age, direct bilirubin, and inorganic phosphate, was able to improve the accuracy of CVD risk assessment up to a high satisfactory value of 0.91. The findings indicate that disorders in the metabolic processes of biological membranes and energy are significantly associated with increased risk of vascular damage in CVD patients. With machine learning methods, the pivotal metabolites and clinical biomarkers offer a promising potential for the efficient risk assessment and diagnosis of CVD.

CeVO4/KB Nanoparticles on Shuttle Effect Inhibition in Lithium-Sulfur Battery Separator Modification.

Lithium-sulfur (Li-S) batteries display promise as redox-based batteries, where separators are an essential part of preventing short-circuiting of the positive and negative electrodes, while the shuttle effect is a critical issue of separators. Currently, commercial PP separators are weak in inhibiting the polysulfides shuttling, so modified separators are needed to inhibit it to improve the battery performance. This paper reports that CeVO4/KB composites act as separator materials. CeVO4/KB modified PP separators enhanced the adsorption of LiPSs, accelerated the rate of Li+ migration, and catalyzed the conversion of LiPSs. These bring about the effect that CeVO4/KB/PP batteries reach 1200.9 mAh g-1 in the first cycle with a capacity retention rate of 86.5% after 100 cycles at 0.2 C and reach 882.7 mAh g-1 of the initial cycle with a capacity decay rate of 0.063% after 1000 cycles at 3 C. This work introduces rare earth metal vanadates to modify the separator, adding new ideas for designing separators for good-performance batteries.

Episodic Transport of Protein Aggregates Achieves a Positive Size Selectivity in Aggresome Formation.

Eukaryotic cells direct toxic misfolded proteins to various protein quality control pathways based on their chemical features and aggregation status. Aggregated proteins are targeted to selective autophagy or specifically sequestered into the "aggresome," a perinuclear inclusion at the microtubule-organizing center (MTOC). However, the mechanism for selectively sequestering protein aggregates into the aggresome remains unclear. To investigate aggresome formation, we reconstituted MTOC-directed aggregate transport in Xenopus laevis egg extract using AgDD, a chemically inducible aggregation system. High-resolution single-particle tracking revealed that dynein-mediated transport of aggregates was highly episodic, with average velocity positively correlated with aggregate size. Our mechanistic model suggests that the recurrent formation of the dynein transport complex biases larger aggregates towards the active transport state, compensating for the slowdown due to viscosity. Both episodic transport and positive size selectivity are specifically associated with aggresome-dynein adaptors. Coupling conventional dynein-activating adaptors to the aggregates perturbs aggresome formation and reverses size selectivity.

Enhancement of ε-poly-L-lysine production by Streptomyces albulus FQF-24 with feeding strategies using cassava starch as carbon source.

ε-Poly-L-lysine (ε-PL) is a natural and wide-spectrum antimicrobial additive. In this study, the production of ε-PL by Streptomyces albulus FQF-24 using cassava starch (CS) as carbon source and the effects of different feeding methods were investigated in a fermenter. The initial shake flask experiments demonstrated the efficient production of ε-PL with CS, achieving the ε-PL production of 1.18 g/L. Subsequent investigations in the fermenter identified that the ideal pH was 3.8 during the ε-PL synthesis phase. Under this condition, the production of ε-PL reached 1.35 g/L. When the pH was maintained at 3.8, the investigation of improvement of feeding composition was carried out in a 5 L fermenter. The intermittent feeding containing CS, inorganic and organic nitrogen sources resulted in the maximum ε-PL production and dry cell weight (DCW) reaching 17.17 g/L and 42.73 g/L. Additionally, continuous feeding with the composition of CS, organic and inorganic nitrogen sources, and inorganic salts further increased ε-PL production and DCW to 27.56 g/L and 38.5 g/L. Summarily, the above results indicate that the fermentation using low-cost CS and continuous feeding strategy with whole medium composition can provide a beneficial reference for the efficient production of ε-PL.

Implantable Zinc Ion Battery and Osteogenesis-Immunoregulation Bifunction of Its Catabolite.

Biocompatible batteries can power implantable electronic devices and have broad applications in medicine. However, the controlled degradation of implantable batteries, the impact of battery catabolites on surrounding tissues, and wireless charging designs are often overlooked. Here, we designed an implantable zinc ion battery (ZIB) using a gelatin/polycaprolactone-based composite gel electrolyte. The prepared ZIBs deliver a high specific capacity of 244.0 mA h g-1 (0.5C) and long cycling stability of 300 cycles (4C). ZIBs were completely degraded within 8 weeks in rats and 30 days in a phosphate-buffered saline lipase solution, demonstrating good biocompatibility and degradability. ZIBs catabolites induced macrophage M2 polarization and exhibited anti-inflammatory properties, with mRNA levels of the M2 markers Arg-1 and CD206 up-regulated 15.8-fold and 13.4-fold, respectively, compared to the blank control group. Meanwhile, the expressions of two typical osteogenic markers, osteopontin and osteocalcin, were up-regulated by 3.6-fold and 5.6-fold, respectively, demonstrating that designed ZIBs promoted osteogenic differentiation of bone marrow mesenchymal stem cells. Additionally, a wireless energy transmission module was designed using 3D printing technology to realize real-time charging of the ZIB in rats. The designed ZIB is a promising power source for implantable medical electronic devices and also serves as a functional material to accelerate bone repair.

Cerium Vanadate Nanozyme with pH-Dependent Dual Enzymatic Activity for Glioblastoma Targeted Therapy and Postradiotherapy Damage Protection.

Nanocatalytic therapy is an emerging technology that uses synthetic nanoscale enzyme mimics for biomedical treatment. However, in the field of neuroscience, achieving neurological protection while simultaneously killing tumor cells is a technical challenge. Herein, we synthesized a biomimic and translational cerium vanadate (CeVO4) nanozyme for glioblastoma (GBM) therapy and the repair of brain damage after GBM ionizing radiation (IR). This system exhibited pH dependence: it showed potent Superoxide dismutase (SOD) enzyme activity in a neutral environment and Peroxidase (POD) enzyme activity in an acidic environment. In GBM cells, this system acted in lysosomes, causing cellular damage and reactive oxygen species (ROS) accumulation; in neuronal cells, this nanozyme could undergo lysosomal escape and nanozyme aggregation with mitochondria, reversing the mitochondrial damage caused by IR and restoring the expression level of the antiapoptotic BCL-2 protein. Mechanistically, we believe that this distribution difference is related to the specific uptake internalization mechanism and lysosomal repair pathway in neurons, and ultimately led to the dual effect of tumor killing and nerve repair in the in vivo model. In summary, this study provides insight into the repair of brain damage after GBM radiation therapy.

Systematic analysis of levels of evidence supporting Chinese clinical practice guidelines for gastrointestinal disease.

BACKGROUND: Clinical practice guidelines (CPGs) inform healthcare decisions and improve patient care. However, an evaluation of guidelines on gastrointestinal diseases (GIDs) is lacking. This study aimed to systematically analyze the level of evidence (LOE) supporting Chinese CPGs for GIDs. METHODS: CPGs for GIDs were identified by systematically searching major databases. Data on LOEs and classes of recommendations (CORs) were extracted. According to the Grades of Recommendation, Assessment, Development, and Evaluation system, LOEs were categorized as high, moderate, low, or very low, whereas CORs were classified as strong or weak. Statistical analyses were conducted to determine the distribution of LOEs and CORs across different subtopics and assess changes in evidence quality over time. FINDINGS: Only 27.9% of these recommendations were supported by a high LOE, whereas approximately 70% were strong recommendations. There was a significant disparity among different subtopics in the proportion of strong recommendations supported by a high LOE. The number of guidelines has increased in the past 5 years, but there has been a concomitant decline in the proportion of recommendations supported by a high LOE. CONCLUSIONS: There is a general lack of high-quality evidence supporting Chinese CPGs for GIDs, and there are inconsistencies in strong recommendations that have not improved. This study identified areas requiring further research, emphasizing the need to bridge these gaps and promote the conduct of high-quality clinical trials. FUNDING: This study was supported by the National Key R&D Program of China (2022YFC2503604 and 2022YFC2503605) and Special Topics in Military Health Care (22BJZ25).

Determination of organic acids for predicting sourness intensity of tea beverage by liquid chromatography-tandem mass spectrometry and chemometrics methods.

The contents of organic acids (OAs) in tea beverage and their relationship with taste intensity have not been fully understood. In this work, a rapid (10 min for a single run) and sensitive (limits of quantification: 0.0044-0.4486 µg/mL) method was developed and validated for the simultaneous determination of 17 OAs in four types of tea, based on liquid chromatography-tandem mass spectrometry with multiple reaction monitoring mode. The contents of 17 OAs in 96 tea samples were measured at levels between 0.01 and 11.80 g/kg (dried weight). Quinic acid, citric acid, and malic acid were determined as the major OAs in green, black, and raw pu-erh teas, while oxalic acid and tartaric acid exhibited the highest contents in ripe pu-erh tea. Taking the OAs composition as input features, a partial least squares regression model was proposed to predict the sourness intensity of tea beverages. The model achieved a root-mean-square error of 0.58 and a coefficient of determination of 0.84 for the testing set. The proposed model provides a theoretical way to evaluate the sensory quality of tea infusion based on its chemical composition.

In Silico Design and Synthesis of Antifungal Peptides Guided by Quantitative Antifungal Activity.

Antifungal peptides (AFPs) are emerging as promising candidates for advanced antifungal therapies because of their broad-spectrum efficacy and reduced resistance development. In silico design of AFPs, however, remains challenging, due to the lack of an efficient and well-validated quantitative assessment of antifungal activity. This study introduced an AFP design approach that leverages an innovative quantitative metric, named the antifungal index (AFI), through a three-step process, i.e., segmentation, single-point mutation, and global multipoint optimization. An exhaustive search of 100 putative AFP sequences indicated that random modifications without guidance only have a 5.97-20.24% chance of enhancing antifungal activity. Analysis of the search results revealed that (1) N-terminus truncation is more effective in enhancing antifungal activity than the modifications at the C-terminus or both ends, (2) introducing the amino acids within the 10-60% sequence region that enhance aromaticity and hydrophobicity are more effective in increasing antifungal efficacy, and (3) incorporating alanine, cysteine, and phenylalanine during multiple point mutations has a synergistic effect on enhancing antifungal activity. Subsequently, 28 designed peptides were synthesized and tested against four typical fungal strains. The success rate for developing promising AFPs, with a minimal inhibitory concentration of ≤5.00 µM, was an impressive 82.14%. The predictive and design tool is accessible at https://antifungipept.chemoinfolab.com.

Efficient Deep Spiking Multilayer Perceptrons With Multiplication-Free Inference.

Advancements in adapting deep convolution architectures for spiking neural networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the inability of multiplication-free inference (MFI) to align with attention and transformer mechanisms, which are critical to superior performance on high-resolution vision tasks, imposes limitations on these gains. To address this, our research explores a new pathway, drawing inspiration from the progress made in multilayer perceptrons (MLPs). We propose an innovative spiking MLP architecture that uses batch normalization (BN) to retain MFI compatibility and introduce a spiking patch encoding (SPE) layer to enhance local feature extraction capabilities. As a result, we establish an efficient multistage spiking MLP network that blends effectively global receptive fields with local feature extraction for comprehensive spike-based computation. Without relying on pretraining or sophisticated SNN training techniques, our network secures a top-one accuracy of 66.39% on the ImageNet-1K dataset, surpassing the directly trained spiking ResNet-34 by 2.67%. Furthermore, we curtail computational costs, model parameters, and simulation steps. An expanded version of our network compares with the performance of the spiking VGG-16 network with a 71.64% top-one accuracy, all while operating with a model capacity 2.1 times smaller. Our findings highlight the potential of our deep SNN architecture in effectively integrating global and local learning abilities. Interestingly, the trained receptive field in our network mirrors the activity patterns of cortical cells.

Electronic structures and quantum capacitance of twisted bilayer graphene with defects based on three-band tight-binding model.

Twisted bilayer graphene (tBLG) with C vacancies would greatly improve the density of states (DOS) around the Fermi level (EF) and quantum capacitance; however, the single-band tight-binding model only considering pz orbitals cannot accurately capture the low-energy physics of tBLG with C vacancies. In this work, a three-band tight-binding model containing three p orbitals of C atoms is proposed to explore the modulation mechanism of C vacancies on the DOS and quantum capacitance of tBLG. We first obtain the hopping integral parameters of the three-band tight-binding model, and then explore the electronic structures and the quantum capacitance of tBLG at a twisting angle of θ = 1.47° under different C vacancy concentrations. The impurity states contributed by C atoms with dangling bonds located around the EF and the interlayer hopping interaction could induce band splitting of the impurity states. Therefore, compared with the quantum capacitance of pristine tBLG (∼18.82 µF cm-2) at zero bias, the quantum capacitance is improved to ∼172.76 µF cm-2 at zero bias, and the working window with relatively large quantum capacitance in the low-voltage range is broadened in tBLG with C vacancies due to the enhanced DOS around the EF. Moreover, the quantum capacitance of tBLG is further increased at zero bias with an increase of the C vacancy concentration induced by more impurity states. These findings not only provide a suitable multi-band tight-binding model to describe tBLG with C vacancies but also offer theoretical insight for designing electrode candidates for low-power consumption devices with improved quantum capacitance.

Copy number gain of FAM131B-AS2 promotes the progression of glioblastoma by mitigating replication stress.

BACKGROUND: Glioblastoma (GBM) is characterized by chromosome 7 copy number gains, notably 7q34, potentially contributing to therapeutic resistance, yet the underlying oncogenes have not been fully characterized. Pertinently, the significance of long noncoding RNAs (lncRNAs) in this context has gained attention, necessitating further exploration. METHODS: FAM131B-AS2 was quantified in GBM samples and cells using qPCR. Overexpression and knockdown of FAM131B-AS2 in GBM cells were used to study its functions in vivo and in vitro. The mechanisms of FAM131B-AS2 were studied using RNA-seq, qPCR, Western blotting, RNA pull-down, coimmunoprecipitation assays, and mass spectrometry analysis. The phenotypic changes that resulted from FAM131B-AS2 variation were evaluated through CCK8 assay, EdU assay, comet assay, and immunofluorescence. RESULTS: Our analysis of 149 primary GBM patients identified FAM131B-AS2, a lncRNA located in the 7q34 region, whose upregulation predicts poor survival. Mechanistically, FAM131B-AS2 is a crucial regulator of the replication stress response, stabilizing replication protein A1 through recruitment of ubiquitin-specific peptidase 7 and activating the ataxia telangiectasia and rad3-related protein kinase pathway to protect single-stranded DNA from breakage. Furthermore, FAM131B-AS2 overexpression inhibited CD8+ T-cell infiltration, while FAM131B-AS2 inhibition activated the cGAS-STING pathway, increasing lymphocyte infiltration and improving the response to immune checkpoint inhibitors. CONCLUSIONS: FAM131B-AS2 emerges as a promising indicator for adjuvant therapy response and could also be a viable candidate for combined immunotherapies against GBMs.

Neuronal Activity Promotes Glioma Progression by Inducing Proneural-to-Mesenchymal Transition in Glioma Stem Cells.

Neuronal activity can drive progression of high-grade glioma by mediating mitogen production and neuron-glioma synaptic communications. Glioma stem cells (GSC) also play a significant role in progression, therapy resistance, and recurrence in glioma, which implicates potential cross-talk between neuronal activity and GSC biology. Here, we manipulated neuronal activity using chemogenetics in vitro and in vivo to study how it influences GSCs. Neuronal activity supported glioblastoma (GBM) progression and radioresistance through exosome-induced proneural-to-mesenchymal transition (PMT) of GSCs. Molecularly, neuronal activation led to elevated miR-184-3p in neuron-derived exosomes that were taken up by GSCs and reduced the mRNA N6-methyladenosine (m6A) levels by inhibiting RBM15 expression. RBM15 deficiency decreased m6A modification of DLG3 mRNA and subsequently induced GSC PMT by activating the STAT3 pathway. Loss of miR-184-3p in cortical neurons reduced GSC xenograft growth, even when neurons were activated. Levetiracetam, an antiepileptic drug, reduced the neuronal production of miR-184-3p-enriched exosomes, inhibited GSC PMT, and increased radiosensitivity of tumors to prolong survival in xenograft mouse models. Together, these findings indicate that exosomes derived from active neurons promote GBM progression and radioresistance by inducing PMT of GSCs. SIGNIFICANCE: Active neurons secrete exosomes enriched with miR-184-3p that promote glioblastoma progression and radioresistance by driving the proneural-to-mesenchymal transition in glioma stem cells, which can be reversed by antiseizure medication levetiracetam.

Hypoxia-Induced Neuronal Activity in Glioma Patients Polarizes Microglia by Potentiating RNA m6A Demethylation.

PURPOSE: Neuronal activity in the brain has been reported to promote the malignant progression of glioma cells via nonsynaptic paracrine and electrical synaptic integration mechanisms. However, the interaction between neuronal activity and the immune microenvironment in glioblastoma (GBM) remains largely unclear. EXPERIMENTAL DESIGN: By applying chemogenetic techniques, we enhanced and inhibited neuronal activity in vitro and in a mouse model to study how neuronal activity regulates microglial polarization and affects GBM progression. RESULTS: We demonstrate that hypoxia drove glioma stem cells (GSC) to produce higher levels of glutamate, which activated local neurons. Neuronal activity promoted GBM progression by facilitating microglial M2 polarization through enriching miR-200c-3p in neuron-derived exosomes, which decreased the expression of the m6A writer zinc finger CCCH-type containing 13 (ZC3H13) in microglia, impairing methylation of dual specificity phosphatase 9 (DUSP9) mRNA. Downregulation of DUSP9 promoted ERK pathway activation, which subsequently induced microglial M2 polarization. In the mouse model, cortical neuronal activation promoted microglial M2 polarization whereas cortical neuronal inhibition decreased microglial M2 polarization in GBM xenografts. miR-200c-3p knockdown in cortical neurons impaired microglial M2 polarization and GBM xenograft growth, even when cortical neurons were activated. Treatment with the anti-seizure medication levetiracetam impaired neuronal activation and subsequently reduced neuron-mediated microglial M2 polarization. CONCLUSIONS: These findings indicated that hypoxic GSC-induced neuron activation promotes GBM progression by polarizing microglia via the exosomal miR-200c-3p/ZC3H13/DUSP9/p-ERK pathway. Levetiracetam, an antiepileptic drug, blocks the abnormal activation of neurons in GBM and impairs activity-dependent GBM progression. See related commentary by Cui et al., p. 1073.

SOC Estimation of Vanadium Redox Flow Batteries Based on the ISCSO-ELM Algorithm.

This study focuses on the stage of charge (SOC) estimation for vanadium redox flow batteries (VFBs), establishing an electrochemical model that provides parameters, including ion concentration. Second, considering the capacity decay of VFBs, an extreme learning machine (ELM) combined with an improved sand cat swarm optimization algorithm, named ISCSO-ELM, is integrated with SOC estimation to predict the battery's SOC more effectively.

Coal Classification Based on Reflection Spectroscopy and the IAT-TELM Algorithm.

As a principal energy globally, coal's quality and variety critically influence the effectiveness of industrial processes. Different coal types cater to specific industrial requirements due to their unique attributes. Traditional methods for coal classification, typically relying on manual examination and chemical assays, lack efficiency and fail to offer consistent accuracy. Addressing these challenges, this work introduces an algorithm based on the reflectance spectrum of coal and machine learning. This method approach facilitates the rapid and accurate classification of coal types through the analysis of coal spectral data. First, the reflection spectra of three types of coal, namely, bituminous coal, anthracite, and lignite, were collected and preprocessed. Second, a model utilizing two hidden layer extreme learning machine (TELM) and affine transformation function is introduced, which is called affine transformation function TELM (AT-TELM). AT-TELM introduces an affine transformation function on the basis of TELM, so that the hidden layer output satisfies the maximum entropy principle and improves the recognition performance of the model. Third, we improve AT-TELM by optimizing the weight matrix and bias of AT-TELM to address the issue of highly skewed distribution caused by randomly assigned weights and biases. The method is named the improved affine transformation function (IAT-TELM). The experimental findings demonstrate that IAT-TELM achieves a remarkable coal classification accuracy of 97.8%, offering a cost-effective, rapid, and precise method for coal classification.

Effects of provisioning on the activity budget and foraging strategies of black-and-white snub-nosed monkeys (Rhinopithecus bieti) in the Baima Snow Mountain Nature Reserve, Yunnan, China.

Provisioning can significantly affect the ranging patterns, foraging strategies, and time budget of wild primates. In this study, we document for the first time, the effects of provisioning on the activity budget and foraging effort in an Asian colobine. Over 3-years, we used an instantaneous scanning method at 10-min intervals to collect data on the activity budget of a semiprovisioned breeding band (SPB) of black-and-white snub-nosed monkeys (Rhinopithecus bieti) (42-70 individuals) at Xiangguqing (Tacheng), Yunnan, China. We then compared the effects of provisioning in our study band with published data on a sympatric wild nonprovisioned breeding band (NPB) of R. bieti (ca. 360 monkeys) at the same field site. The SPB spent 25.6% of their daytime feeding, 17.1% traveling, 46.9% resting, and 10.3% socializing. In comparison, the NPB devoted more time to feeding (34.9%) and socializing (14.1%), less time to resting (31.3%), and was characterized by a greater foraging effort (1.74 versus 0.96, foraging effort = (feeding + traveling)/resting; see Methods). There was no difference between bands in the proportion of their activity budget devoted to traveling (15.7% vs. 17.1%). In addition, the SPB exhibited a more consistent activity budget and foraging effort across all seasons of the year compared to the NPB. These findings suggest that the distribution, availability, and productivity of naturally occurring feeding sites is a major determinant of the behavioral strategies and activity budget of R. bieti. Finally, a comparison of our results with data on six nonprovisioned R. bieti bands indicates that caution must be raised in meta-analyses or intraspecific comparisons of primate behavioral ecology that contain data generated from both provisioned and nonprovisioned groups.

Mesenchymal stem cells, as glioma exosomal immunosuppressive signal multipliers, enhance MDSCs immunosuppressive activity through the miR-21/SP1/DNMT1 positive feedback loop.

BACKGROUND: The immunosuppressive microenvironment in glioma induces immunotherapy resistance and is associated with poor prognosis. Glioma-associated mesenchymal stem cells (GA-MSCs) play an important role in the formation of the immunosuppressive microenvironment, but the mechanism is still not clear. RESULTS: We found that GA-MSCs promoted the expression of CD73, an ectonucleotidase that drives immunosuppressive microenvironment maintenance by generating adenosine, on myeloid-derived suppressor cells (MDSCs) through immunosuppressive exosomal miR-21 signaling. This process was similar to the immunosuppressive signaling mediated by glioma exosomal miR-21 but more intense. Further study showed that the miR-21/SP1/DNMT1 positive feedback loop in MSCs triggered by glioma exosomal CD44 upregulated MSC exosomal miR-21 expression, amplifying the glioma exosomal immunosuppressive signal. Modified dendritic cell-derived exosomes (Dex) carrying miR-21 inhibitors could target GA-MSCs and reduce CD73 expression on MDSCs, synergizing with anti-PD-1 monoclonal antibody (mAb). CONCLUSIONS: Overall, this work reveals the critical role of MSCs in the glioma microenvironment as signal multipliers to enhance immunosuppressive signaling of glioma exosomes, and disrupting the positive feedback loop in MSCs with modified Dex could improve PD-1 blockade therapy.

Correction: Theoretical predictions and experimental verifications of SERS detection in colorants.

[This corrects the article DOI: 10.1039/D3RA01584J.].

PFCE2: A versatile parameter-free calibration enhancement framework for near-infrared spectroscopy.

Near-infrared (NIR) spectroscopy is a widely used technique for chemical analysis, but it has faced challenges of calibration transfer, maintenance, and enhancement among different instruments and conditions. The parameter-free calibration enhancement (PFCE) framework was developed to address these challenges with non-supervised (NS), semi-supervised (SS), and full-supervised (FS) methods. This study presented PFCE2, an updated version of the PFCE framework that incorporates two new constraints and a new method to improve the robustness and efficiency of calibration enhancement. First, normalized L2 and L1 constraints were introduced to replace the correlation coefficient (Corr) constraint used in the original PFCE. These constraints preserve the parameter-free feature of PFCE and impose smoothness or sparsity on the model coefficients. Second, multitask PFCE (MT-PFCE) was proposed within the framework to address the calibration enhancement among multiple instruments, enabling the framework to be versatile for all possible calibration transfer situations. Demonstrations conducted on three NIR datasets of tablets, plant leaves, and corn showed that the PFCE methods with the new L2 and L1 constraints can result in more accurate and robust predictions than the Corr constraint, especially when the standard sample size is small. Moreover, MT-PFCE could refine all models in the involved scenarios at once, leading to significant enhancement in model performance, compared to the original PFCE method with the same data requirements. Finally, the applicable situations of the PFCE framework and other analogous calibration transfer methods were summarized, facilitating users to choose suitable methods for their application. The source codes written in both MATLAB and Python are available at https://github.com/JinZhangLab/PFCE and https://pypi.org/project/pynir/, respectively.