Excess Potassium Promotes Autophagy to Maintain the Immunosuppressive Capacity of Myeloid-Derived Suppressor Cells Independent of Arginase 1.

Potassium ions (K+) are critical electrolytes that regulate multiple functions in immune cells. Recent studies have shown that the elevated concentration of extracellular potassium in the tumor interstitial fluid limits T cell effector function and suppresses the anti-tumor capacity of tumor-associated macrophages (TAMs). The effect of excess potassium on the biology of myeloid-derived suppressor cells (MDSCs), another important immune cell component of the tumor microenvironment (TME), is unknown. Here, we present data showing that increased concentrations of potassium chloride (KCl), as the source of K+ ions, facilitate autophagy by increasing the expression of the autophagosome marker LC3ß. Simultaneously, excess potassium ions significantly decrease the expression of arginase I (Arg I) and inducible nitric oxide synthase (iNOS) without reducing the ability of MDSCs to suppress T cell proliferation. Further investigation reveals that excess K+ ions decrease the expression of the transcription factor C/EBP-ß and alter the expression of phosphorylated kinases. While excess K+ ions downregulated the expression levels of phospho-AMPKα (pAMPKα), it increased the levels of pAKT and pERK. Additionally, potassium increased mitochondrial respiration as measured by the oxygen consumption rate (OCR). Interestingly, all these alterations induced by K+ ions were abolished by the autophagy inhibitor 3-methyladenine (3-MA). Our results suggest that hyperosmotic stress caused by excess K+ ions regulate the mitochondrial respiration and signaling pathways in MDSCs to trigger the process of autophagy to support MDSCs' immunosuppressive function by mechanisms independent of Arg I and iNOS. Overall, our in vitro and ex vivo findings offer valuable insights into the adaptations of MDSCs within the K+ ion-rich TME, which has important implications for MDSCs-targeted therapies.

Structural and functional whole brain changes in autism spectrum disorder at different age stages.

Autism spectrum disorder (ASD) is a developmental disorder involving regional changes and local neural disturbances. However, few studies have investigated the dysfunctional phenomenon across different age stages. This study explores the structural and functional brain changes across different developmental stages in individuals with ASD, focusing on childhood (6-12 years), adolescence (12-18 years), and adulthood (18 + years). Using a comprehensive set of neuroimaging metrics, including modulated and non-modulated voxel-based morphometry (VBM), regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and fractional ALFF (fALFF), we identified significant stage-specific alterations in both VBM and functional measurements. Our results reveal that ASD is associated with progressive and stage-specific abnormalities in brain structure and function, with distinct patterns emerging at each developmental stage. Specifically, we observed significant modulated VBM reductions in the precuneus, lentiform nucleus, and inferior parietal lobule, accompanied by increases in the midbrain and sub-gyral regions. Moreover, we observed unmodulated VBM increment in regions including lentiform nucleus and thalamus. Functionally, ReHo analyses demonstrated disrupted local synchronization in the medial frontal gyrus, while ALFF and fALFF metrics highlighted altered spontaneous brain activity in the sub-gyral and sub-lobar. Finally, correlation analyses revealed that stage-specific findings are closely linked to clinical social- and behavior-related scores, with VBM in the inferior parietal lobule and putamen as well as ReHo in supplemental motor area being significantly associated with restrictive repetitive behaviors in childhood. These findings underscore the importance of considering age-specific brain changes when studying ASD and suggest that targeted interventions may be necessary at different developmental stages.

Quantitative Characterization of Zebrafish Caudal Fin Regeneration Based on Mueller Matrix OCT In Vivo.

Zebrafish serves as a valuable model for studying tissue regeneration due to their comprehensive regenerative abilities, particularly in bone tissue. In this study, a Mueller matrix optical coherence tomography (OCT) system was applied to monitor the regenerative processes of zebrafish caudal fins in vivo. The analysis focused on evaluating the thickness of the caudal fin tip and the distribution of internal bone tissue during the regenerative process. Subsequently, the effect of ectoine solution on the regeneration process was observed and discussed. Our findings revealed that the caudal fin blastema did not exhibit phase-induced polarization characteristics in the Mueller matrix OCT images. Statistical analyses indicated that the caudal fins did not fully regenerate to their original state within 21 days. Furthermore, the results suggested that ectoine solution could enhance tissue regeneration. This approach provides a method for quantifying zebrafish caudal fin regeneration and advances observation techniques for biomedical and clinical applications.

Finite element study on the micromechanics of cement-augmented proximal femoral nail anti-rotation (PFNA) for intertrochanteric fracture treatment.

Blade cut-out is a common complication when using proximal femoral nail anti-rotation (PFNA) for the treatment of intertrochanteric fractures. Although cement augmentation has been introduced to overcome the cut-out effect, the micromechanics of this approach remain to be clarified. While previous studies have developed finite element (FE) models based on lab-prepared or cadaveric samples to study the cement-trabeculae interface, their demanding nature and inherent disadvantages limit their application. The aim of this study was to develop a novel 'one-step forming' method for creating a cement-trabeculae interface FE model to investigate its micromechanics in relation to PFNA with cement augmentation. A human femoral head was scanned using micro-computed tomography, and four volume of interest (VOI) trabeculae were segmented. The VOI trabeculae were enclosed within a box to represent the encapsulated region of bone cement using ANSYS software. Tetrahedral meshing was performed with Hypermesh software based on Boolean operation. Finally, four cement-trabeculae interface FE models comprising four interdigitated depths and five FE models comprising different volume fraction were established after element removal. The effects of friction contact, frictionless contact, and bond contact properties between the bone and cement were identified. The maximum micromotion and stress in the interdigitated and loading bones were quantified and compared between the pre- and post-augmentation situations. The differences in micromotion and stress with the three contact methods were minimal. Micromotion and stress decreased as the interdigitation depth increased. Stress in the proximal interdigitated bone showed a correlation with the bone volume fraction (R2 = 0.70); both micromotion (R2 = 0.61) and stress (R2 = 0.93) at the most proximal loading region exhibited a similar correlation tendency. When comparing the post- and pre-augmentation situations, micromotion reduction in the interdigitated bone was more effective than stress reduction, particularly near the cement border. The cementation resulted in a significant reduction in micromotion within the loading bone, while the decrease in stress was minimal. Noticeable gradients of displacement and stress reduction can be observed in models with lower bone volume fraction (BV/TV). In summary, cement augmentation is more effective at reducing micromotion rather than stress. Furthermore, the reinforcing impact of bone cement is particularly prominent in cases with a low BV/TV. The utilization of bone cement may contribute to the stabilization of trabecular bone and PFNA primarily by constraining micromotion and partially shielding stress.

Prognostic significance of collagen signatures in pancreatic ductal adenocarcinoma obtained from second-harmonic generation imaging.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) ranks among the deadliest types of cancer, and it will be meaningful to search for new biomarkers with prognostic value to help clinicians tailor therapeutic strategies. METHODS: Here we tried to use an advanced optical imaging technique, multiphoton microscopy (MPM) combining second-harmonic generation (SHG) and two-photon excited fluorescence (TPEF) imaging, for the label-free detection of PDAC tissues from a cohort of 149 patients. An automated image processing method was used to extract collagen features from SHG images and the Kaplan-Meier survival analysis and Cox proportional hazards regression were used to assess the prognostic value of collagen signatures. RESULTS: SHG images clearly show the different characteristics of collagen fibers in tumor microenvironment. We gained eight collagen morphological features, and a Feature-score was derived for each patient by the combination of these features using ridge regression. Statistical analyses reveal that Feature-score is an independent factor, and can predict the overall survival of PDAC patients as well as provide well risk stratification. CONCLUSIONS: SHG imaging technique can potentially be a tool for the accurate diagnosis of PDAC, and this optical biomarker (Feature-score) may help clinicians make more approximate treatment decisions.

The Relationship Between Composite Inflammatory Ratios and Complications of Massive Ischaemic Stroke.

OBJECTIVE: To investigate the relationship between complications of massive cerebral infarction (MCI) and composite inflammatory ratios. STUDY DESIGN: A case-control study. Place and Duration of the Study: Department of Neurology, Affiliated Hospital of Putian University, Putian, China, from January 2019 to November 2021. METHODOLOGY: Eighty-two patients with MCI underwent blood tests within 24 hours of admission. Complications such as cerebral herniation, haemorrhage transformation (HT), and stroke-associated pneumonia (SAP) were evaluated based on imaging examinations. The prognosis was assessed using the modified Rankin Scale score (mRS) at discharge. RESULTS: Among the 82 patients, the cerebral herniation group had higher levels of systemic immune inflammation index (SII) and neutrophil-to-lymphocyte ratio (NLR) compared to the non-cerebral herniation group. MCI patients who developed HT had higher levels of SII, NLR, mean platelet volume/platelet (MPV/PLT), and platelet-to-lymphocyte ratio (PLR). The SAP group had higher levels of MPV/PLT and NLR compared to the non-SAP group. The poor prognosis group had higher SII and NLR levels but a lower lymphocyte-to-monocyte ratio (LMR) compared to the good prognosis group. CONCLUSION: NLR showed high accuracy in predicting complications and the short-term prognosis of MCI. SII was linked to cerebral herniation, HT, and the short-term prognosis of MCI. MPV/PLT was found to be related to SAP and HT caused by MCI. LMR may act as a protective factor for the short-term prognosis of MCI. KEY WORDS: Massive cerebral infarction, Neutrophil-to-lymphocyte ratio, Systemic immune inflammation index, Prognosis.

Gene-gene interaction network analysis indicates CNTN2 is a candidate gene for idiopathic generalized epilepsy.

Twin and family studies have established the genetic contribution to idiopathic generalized epilepsy (IGE). The genetic architecture of IGE is generally complex and heterogeneous, and the majority of the genetic burden in IGE remains unsolved. We hypothesize that gene-gene interactions contribute to the complex inheritance of IGE. CNTN2 (OMIM* 615,400) variants have been identified in cases with familial adult myoclonic epilepsy and other epilepsies. To explore the gene-gene interaction network in IGE, we took the CNTN2 gene as an example and investigated its co-occurrent genetic variants in IGE cases. We performed whole-exome sequencing in 114 unrelated IGE cases and 296 healthy controls. Variants were qualified with sequencing quality, minor allele frequency, in silico prediction, genetic phenotype, and recurrent case numbers. The STRING_TOP25 gene interaction network analysis was introduced with the bait gene CNTN2 (denoted as A). The gene-gene interaction pair mode was presumed to be A + c, A + d, A + e, with a leading gene A, or A + B + f, A + B + g, A + B + h, with a double-gene A + B, or other combinations. We compared the number of gene interaction pairs between the case and control groups. We identified three pairs in the case group, CNTN2 + PTPN18, CNTN2 + CNTN1 + ANK2 + ANK3 + SNTG2, and CNTN2 + PTPRZ1, while we did not discover any pairs in the control group. The number of gene interaction pairs in the case group was much more than in the control group (p = 0.021). Taking together the genetic bioinformatics, reported epilepsy cases, and statistical evidence in the study, we supposed CNTN2 as a candidate pathogenic gene for IGE. The gene interaction network analysis might help screen candidate genes for IGE or other complex genetic disorders.

Exploration of the relationship between tumor-infiltrating lymphocyte score and histological grade in breast cancer.

BACKGROUND: The histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients. METHODS: In this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs. RESULTS: Statistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648-3.941; P < 0.0001), but TILs-WG is not an independent predictive factor for grade (P > 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3. CONCLUSION: Our results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients.

Computer-Aided Multiphoton Microscopy Diagnosis of 5 Different Primary Architecture Subtypes of Meningiomas.

Meningiomas rank among the most common intracranial tumors, and surgery stands as the primary treatment modality for meningiomas. The precise subtyping and diagnosis of meningiomas, both before and during surgery, play a pivotal role in enabling neurosurgeons choose the optimal surgical program. In this study, we utilized multiphoton microscopy (MPM) based on 2-photon excited fluorescence and second-harmonic generation to identify 5 common meningioma subtypes. The morphological features of these subtypes were depicted using the MPM multichannel mode. Additionally, we developed 2 distinct programs to quantify collagen content and blood vessel density. Furthermore, the lambda mode of the MPM characterized architectural and spectral features, from which 3 quantitative indicators were extracted. Moreover, we employed machine learning to differentiate meningioma subtypes automatically, achieving high classification accuracy. These findings demonstrate the potential of MPM as a noninvasive diagnostic tool for meningioma subtyping and diagnosis, offering improved accuracy and resolution compared with traditional methods.

Improving the diagnosis of ductal carcinoma in situ with microinvasion without immunohistochemistry: An innovative method with H&E-stained and multiphoton microscopy images.

Ductal carcinoma in situ with microinvasion (DCISM) is a challenging subtype of breast cancer with controversial invasiveness and prognosis. Accurate diagnosis of DCISM from ductal carcinoma in situ (DCIS) is crucial for optimal treatment and improved clinical outcomes. However, there are often some suspicious small cancer nests in DCIS, and it is difficult to diagnose the presence of intact myoepithelium by conventional hematoxylin and eosin (H&E) stained images. Although a variety of biomarkers are available for immunohistochemical (IHC) staining of myoepithelial cells, no single biomarker is consistently sensitive to all tumor lesions. Here, we introduced a new diagnostic method that provides rapid and accurate diagnosis of DCISM using multiphoton microscopy (MPM). Suspicious foci in H&E-stained images were labeled as regions of interest (ROIs), and the nuclei within these ROIs were segmented using a deep learning model. MPM was used to capture images of the ROIs in H&E-stained sections. The intensity of two-photon excitation fluorescence (TPEF) in the myoepithelium was significantly different from that in tumor parenchyma and tumor stroma. Through the use of MPM, the myoepithelium and basement membrane can be easily observed via TPEF and second-harmonic generation (SHG), respectively. By fusing the nuclei in H&E-stained images with MPM images, DCISM can be differentiated from suspicious small cancer clusters in DCIS. The proposed method demonstrated good consistency with the cytokeratin 5/6 (CK5/6) myoepithelial staining method (kappa coefficient = 0.818).

Prognostic significance of collagen signatures at breast tumor boundary obtained by combining multiphoton imaging and imaging analysis.

PURPOSE: Collagen features in breast tumor microenvironment is closely associated with the prognosis of patients. We aim to explore the prognostic significance of collagen features at breast tumor border by combining multiphoton imaging and imaging analysis. METHODS: We used multiphoton microscopy (MPM) to label-freely image human breast tumor samples and then constructed an automatic classification model based on deep learning to identify collagen signatures from multiphoton images. We recognized three kinds of collagen signatures at tumor boundary (CSTB I-III) in a small-scale, and furthermore obtained a CSTB score for each patient based on the combined CSTB I-III by using the ridge regression analysis. The prognostic performance of CSTB score is assessed by the area under the receiver operating characteristic curve (AUC), Cox proportional hazard regression analysis, as well as Kaplan-Meier survival analysis. RESULTS: As an independent prognostic factor, statistical results reveal that the prognostic performance of CSTB score is better than that of the clinical model combining three independent prognostic indicators, molecular subtype, tumor size, and lymph nodal metastasis (AUC, Training dataset: 0.773 vs. 0.749; External validation: 0.753 vs. 0.724; HR, Training dataset: 4.18 vs. 3.92; External validation: 4.98 vs. 4.16), and as an auxiliary indicator, it can greatly improve the accuracy of prognostic prediction. And furthermore, a nomogram combining the CSTB score with the clinical model is established for prognosis prediction and clinical decision making. CONCLUSION: This standardized and automated imaging prognosticator may convince pathologists to adopt it as a prognostic factor, thereby customizing more effective treatment plans for patients.

Controllable Adaptive Molybdate-Oligosaccharide Nanoparticles Regulate M2 Macrophage Mitochondrial Function and Promote Angiogenesis via PI3K/HIF-1α/VEGF Pathway to Accelerate Diabetic Wound Healing.

The complex wound environment of diabetic wounds leads to poor treatment efficacy, and the inflammatory disorders and vascular injury are the primary causes of death in such patients. Herein, a sprayable, controllable adaptive, pH-responsive nanosystem of molybdate and oligosaccharide (CMO) is specially developed as an immunomodulatory and angiogenesis-promotion material for diabetic wound healing. CMO exhibited pH-responsive release of Mo2+ and oligosaccharide (COS), specifically in response to the alkalescent environment observed in diabetic wounds. CMO provide an anti-inflammatory environment by promoting M2 polarization through significantly stimulating macrophage mitochondrial function. Specifically, CMO with a certain concentration reduce reactive oxygen species (ROS) and tumor necrosis factor α (TNF-α) expression, and upregulated mitochondrial membrane potential (MMP), superoxide dismutase (SOD), and interleukin 10 (IL-10) expression in macrophages. Moreover, CMO facilitate angiogenesis via upregulating the PI3K/HIF-1α/VEGF pathway-a critical process for the formation of new blood vessels that supply nutrients and oxygen to the healing tissue. Remarkably, CMO promote cell viability and migration of endothelial cells, and enhance the expression of angiogenic genes. In vitro and in vivo studies suggest this simple but powerful nanosystem targeting mitochondrial function has the potential to become an effective treatment for diabetic wound healing.

Core-Satellite Nanoassemblies as SPR/SERS Dual-Mode Plasmonic Sensors for Sensitively Detecting Ractopamine in Complex Media.

Highly sensitive and reliable detection of ß-adrenergic agonists is especially necessary due to the illegal abuse of growth-promoting feed additives. Here, we develop a novel surface plasmon resonance/surface-enhanced Raman scattering (SPR/SERS) dual-mode plasmonic sensor based on core-satellite nanoassemblies for the highly sensitive and reliable detection of ractopamine (RAC). The addition of RAC results in the decomposition of core-satellite nanoassemblies and consequently changes the Rayleigh scattering color of dark-field microscopy (DFM) images and the Raman scattering intensity of SERS spectra. The excellent sensitivity, specificity, and uniformity of this strategy were confirmed by detecting RAC in various complex media in the farm-to-table chain, and the limit of detection (LOD) was 0.03 ng/mL in an aqueous solution. In particular, the convenient access to livestock sewage not only ensures animal welfare but also provides great convenience for the market regulation of ß-agonists. The success of our on-site strategy only with a portable Raman device promises great application prospects for ß-agonist detection.

An adjustable Predictive&Prescriptive method for the RO-based optimal power flow problem.

Traditionally before solving the optimal power flow considering uncertainty (OPF-U) problem, the predicted value of uncertainty parameters, such as wind power, e.g., is derived from data using a statistics approach or machine learning. Based on the predicted uncertainty parameters, the solution to the OPF-U problem can be obtained by the prescriptive analytics technique, such as robust optimization (RO). However, it is unclarified how the prediction error in predictive analytics affects solving the OPF-U problem in prescriptive analytics. We propose an adjustable framework method combining machine learning and RO for the OPF-U problem. The k-nearest neighbor is applied to obtain k samples around the predicted value from sufficient historical data. And the optimization results from a minimum volume ellipsoid set containing the k samples are applied to construct KMV set. Then a robust fluctuation region with an adjustable budget level is gained from the KMV set by a two-term exponential formula, which can be embedded into a two-stage RO model. Computational experiments under test cases of different uncertainty scales show the robustness and adjustability of the proposed fluctuation region are better than the state-of-the-art box and ellipsoidal sets. The solution of the proposed two-stage RO model is more economical than the state-of-the-art RO model. The out-of-sample simulation also demonstrates the proposed adjustable Predictive&Prescriptive method can reduce the computational burden as the scale of the system increases when predictive and prescriptive analytics are separated.

Liquid Interfacial Coassembly of Plasmonic Arrays and Trace Hydrophobic Nanoplastics in Edible Oils for Robust Identification and Classification by Surface-Enhanced Raman Spectroscopy.

The ubiquity of micro-/nanoplastics poses a visible threat to the environment, aquatic organisms, and human beings and has become a global concern. Here, we proposed a liquid interface-based strategy using surface-enhanced Raman spectroscopy to coassemble nanoplastics and gold nanoparticles into a dense and homogeneous plasmonic array, thereby enabling the rapid and sensitive detection of trace nanoplastics. In addition, due to the uniqueness of the oil-water immiscible two-phase interface, we achieved ideal results for the detection of nanoplastics in a complex matrix (e.g., aqueous environment and edible oil) with a detection limit of µg/mL. With the aid of the principal component analysis algorithm, the differentiation and identification of multiple nanoplastic components (e.g., polystyrene, polyethylene, and polyethylene terephthalate) in aqueous environments and common hazards (e.g., Bap and Phe) in edible oil were achieved. Therefore, our self-assembled plasmonic arrays are expected to be used for monitoring environmental pollution and food safety.

Intrinsic SERS Fingerprints of Aptamer-Peptide Conjugates for Direct High-Specific Profiling Abnormal Protein Levels in Cancer Patients.

Surface-enhanced Raman spectroscopy (SERS) with ultrasensitive vibrational fingerprints enables quick identification and trace detection of various kinds of molecules. But proteins usually have low Raman cross sections and are difficult to generate recognizable signals in direct SERS detection. Recently, nucleic acid-peptide conjugates are emerging with great potential in structuring, assembling, catalyzing, sensing, etc., and the coupling of aptamers further enables superior biological recognition and programmability. Here, we develop the aptamer-peptide conjugates as a new kind of SERS probe for direct high-specific profiling abnormal protein levels in cancer patients. The aptamer conjugated with glutathione (GSH) functions as both the recognition element and the SERS reporters that can simultaneously generate SERS fingerprints of both peptides and nucleic acids. This kind of biocompatible probe appears to have excellent performance in high-salt environments and realizes rapid, simple, and multisignal detection of thrombin (TB). Data-driven soft independent modeling of class analogy (DD-SIMCA) is used to distinguish SERS profiles of actual blood samples and realize the identification and classification of cancer patients. Furthermore, the effect of low-temperature storage time on blood samples is analyzed by tracking the changes of SERS profiles; the results hint that plasma samples stored under 4 °C for more than 2 days could generate false negative results due to TB hydrolysis, which has important implications for clinical sample analysis. This kind of nucleic acid-peptide conjugate provides new ideas for SERS sensing strategy in the future.

Studying trabecular bone samples demonstrates a power law relation between deteriorated structure and mechanical properties - a study combining 3D printing with the finite element method.

Introduction: The bone volume fraction (BV/TV) significantly contributes to the mechanical properties of trabecular bone. However, when studies compare normal trabeculae against osteoporotic trabeculae (in terms of BV/TV decrease), only an "average" mechanical result has been determined because of the limitation that no two trabecular structures are the same and that each unique trabecular structure can be mechanically tested only once. The mathematic relation between individual structural deterioration and mechanical properties during aging or the osteoporosis process has yet to be further clarified. Three-dimensional (3D) printing and micro-CT-based finite element method (µFEM) can assist in overcoming this issue. Methods: In this study, we 3D printed structural-identical but BV/TV value-attenuated trabecular bones (scaled up ×20) from the distal femur of healthy and ovariectomized rats and performed compression mechanical tests. Corresponding µFEM models were also established for simulations. The tissue modulus and strength of 3D printed trabecular bones as well as the effective tissue modulus (denoted as Ez) derived from µFEM models were finally corrected by the side-artifact correction factor. Results: The results showed that the tissue modulus corrected, strength corrected and Ez corrected exhibited a significant power law function of BV/TV in structural-identical but BV/TV value-attenuated trabecular samples. Discussion: Using 3D printed bones, this study confirms the long-known relationship measured in trabecular tissue with varying volume fractions. In the future, 3D printing may help us attain better bone strength evaluations and even personal fracture risk assessments for patients who suffer from osteoporosis.

Prognostic value of tumor necrosis based on the evaluation of frequency in invasive breast cancer.

BACKGROUND: Tumor necrosis (TN) was associated with poor prognosis. However, the traditional classification of TN ignored spatial intratumor heterogeneity, which may be associated with important prognosis. The purpose of this study was to propose a new method to reveal the hidden prognostic value of spatial heterogeneity of TN in invasive breast cancer (IBC). METHODS: Multiphoton microscopy (MPM) was used to obtain multiphoton images from 471 patients. According to the relative spatial positions of TN, tumor cells, collagen fibers and myoepithelium, four spatial heterogeneities of TN (TN1-4) were defined. Based on the frequency of individual TN, TN-score was obtained to investigate the prognostic value of TN. RESULTS: Patients with high-risk TN had worse 5-year disease-free survival (DFS) than patients with no necrosis (32.5% vs. 64.7%; P < 0.0001 in training set; 45.8% vs. 70.8%; P = 0.017 in validation set), while patients with low-risk TN had a 5-year DFS comparable to patients with no necrosis (60.0% vs. 64.7%; P = 0.497 in training set; 59.8% vs. 70.8%; P = 0.121 in validation set). Furthermore, high-risk TN "up-staged" the patients with IBC. Patients with high-risk TN and stage I tumors had a 5-year DFS comparable to patients with stage II tumors (55.6% vs. 62.0%; P = 0.565 in training set; 62.5% vs. 66.3%; P = 0.856 in validation set), as well as patients with high-risk TN and stage II tumors had a 5-year DFS comparable to patients with stage III tumors (33.3% vs. 24.6%; P = 0.271 in training set; 44.4% vs. 39.3%; P = 0.519 in validation set). CONCLUSIONS: TN-score was an independent prognostic factor for 5-year DFS. Only high-risk TN was associated with poor prognosis. High-risk TN "up-staged" the patients with IBC. Incorporating TN-score into staging category could improve its performance to stratify patients.

Prolonged fluoride exposure induces spatial-memory deficit and hippocampal dysfunction by inhibiting small heat shock protein 22 in mice.

Millions of residents in areas with high-fluoride drinking water supply ingest excessive levels of fluoride for long periods. This study investigated the mechanisms and impacts of lifelong exposure to naturally occurring moderate-high-fluoride drinking water on spatial-memory function by studying mice in controlled experiments. Spatial-memory deficits and disorders of hippocampal neuronal electrical activity were observed in mice exposed to 25-ppm or 50-ppm-fluoride drinking water for 56 weeks, but not in adult or old mice exposed to 50 ppm fluoride for 12 weeks. Ultrastructural analysis showed severely damaged hippocampal mitochondria, evidenced by reduced mitochondrial membrane potential and ATP content. Mitochondrial biogenesis was impaired in fluoride-exposed mice, manifesting as a significantly reduced mtDNA content, mtDNA-encoded subunits mtND6 and mtCO1, and respiratory complex activities. Fluoride reduced expression of Hsp22, a beneficial mediator of mitochondrial homeostasis, and decreased levels of signaling for the PGC-1α/TFAM pathway-which regulates mitochondrial biogenesis-and the NF-κß/STAT3 pathway-which regulates mitochondrial respiratory chain enzyme activity. Hippocampus-specific Hsp22-overexpression improved fluoride-induced spatial-memory deficits by activating the PGC-1α/TFAM and STAT3 signaling pathways, while Hsp22-silencing aggravated the deficits by inhibiting both pathways. Downregulation of Hsp22 plays a vital role in fluoride-induced spatial-memory deficits by impacting mtDNA-encoding subsets and mitochondrial respiratory chain enzyme activity.

Combining the guidelines and multiphoton imaging methods to improve the prognostic value of tumor-infiltrating lymphocytes in breast cancer.

Multiphoton microscopy (MPM) was introduced to label-freely obtain tumor-infiltrating lymphocytes (TILs) images from a total of 611 patients, and the prognostic value of TILs in breast cancer was assessed by the MPM method (TILs-MPM) and guidelines method proposed by the International Immuno-Oncology Biomarker Working Group (TILs-WG), respectively. Moreover, the clinical (CLI) model, TILs-WG + TILs-MPM model, and full model (CLI + TILs-WG + TILs-MPM) were developed to investigate the prognostic value of TILs. The results show that TILs-WG performs better in estrogen receptor (ER)-negative subgroup, and TILs-MPM is comparable with TILs-WG in the ER-negative subgroup, but has the best performance in the ER-positive subgroup. Furthermore, the TILs-WG + TILs-MPM model can significantly improve the prognostic power compared with the TILs-WG model, and the full model has excellent performance, with high area under the curve (AUC) and hazard ratio (HR) in both ER-positive, ER-negative subgroups, and the complete cohort. Our results suggest that the combination of TILs-WG with TILs-MPM model can greatly improve the prognostic value of TILs.