Restoration of hemimandibulectomy defects following tumour extirpation to restore oral function is a herculean task for practitioners. Prosthetic treatment alternatives available for rehabilitation of acquired hemimandibulectomy defects according to mandibular reconstruction type and extent (Cantor-Curtis classification) are unclear. This systematic review aims to assess the spectrum of prosthodontic rehabilitation approaches with regard to reconstruction type and extent of mandibular surgical defects. The databases incorporated for literature search were Google Scholar and Medline (PubMed). Relevant search terms for hemimandibulectomy and reconstruction with prosthetic rehabilitation were used. Two reviewers independently assessed the articles using eligibility criteria; published case reports and case series in the English language and depicting prosthodontic treatment modality of patients greater than 15 years were included. A total of 202 records were identified from the database search of which 19 duplicates were removed. The remaining articles were assessed for eligibility, and 55 articles (comprising 58 cases) were finally included in the study. This review revealed various prosthetic alternatives ranging from guide flange, twin occlusion, palatal ramp, conventional to hybrid partial and complete dentures to implant-supported prosthesis including a few innovative prosthetic approaches. This systematic review provides a plethora of prosthodontic rehabilitation approaches according to the extent of hemimandibular surgical defect and type of reconstruction. This will facilitate practitioners and prosthodontists in sequential treatment planning and management of hemimandibulectomy cases in their routine practice. Surgical excision of various malignant or benign tumours like ameloblastoma, osteosarcoma and various injuries of the maxilla and/or mandible has resulted in partial or total maxillofacial defects involving hard and soft tissues. These conditions deteriorate oral function, aesthetics and comfort leading to impaired quality of life. Due to extensive surgical resection, the surface area required for adequate retention of the prosthesis is remarkably reduced. The radiotherapy adjunct with surgery further deteriorates the load-bearing capacity of the underlying denture-supporting tissue, and thus, the prosthodontic rehabilitation of these patients becomes an uphill task. Various classifications of hemimandibulectomy defects based on the nature and extent of mandible resection are available, but the Cantor and Curtis (CC) classification devised in the 1970s was widely observed in the majority of the articles studied. This system classifies defects based on remaining structures into six classes [Restoration of the hemimandibular defect is a multidisciplinary approach involving an onco-surgeon, oral maxillofacial surgeon, prosthodontist, speech therapist, physiotherapist, etc. Treatment varies according to the type of mandibular reconstruction . Various prosthetic options are available including implant-supported prosthesis, endoprosthesis, guiding flange, twin occlusion prosthesis, etc. to improve a patient’s mastication, speech, aesthetics and lifestyle . DespiteMethodologyReview ProtocolThe systematic literature search was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines  and was Literature Search StrategyAn initial search was conducted on June 30, 2021, by using electronic databases of Medline (PubMed) and Google Scholar by two independent researchers (SK and SS) for published articles from January 1, 2010, to June 30, 2021, as per inclusion criteria. The database Medline (PubMed) was searched with the following keywords for Medical Subject Headings (MeSH) terms: "hemimandibulectomy", "rehabilitation", "prosthetic" and combinations of these keywords were used for Google Scholar with appropriate filters  according to the inclusion criteria, and those with unclear methodology were included in the full-text assessment Table .Risk of Bias AssessmentQuality assessment of all the relevant studies included in the present review was performed by two reviewers (HD and BS) according to the Joanna Briggs Institute  . This JBData ExtractionThe relevant studies obtained following screening were categorised into two groups: case reports and case series. Two reviewers (HD and SK) accomplished data extraction individually, while AJ checked the data: author name, year of publication, JBI score, age, gender, extent of defect (CC classification), name and type of prosthesis in both arches, reconstruction type (if any), surgical scarring, radiotherapy, follow-up period and adverse effect.ResultsStudy CharacteristicsThe initial literature search from the selected databases revealed 202 records from which 19 duplicates were identified and removed. After the screening of titles and abstracts, 55 articles (58 cases) with moderate to low risk of bias were finally included after quality assessment  with hemimandibular defect according to the Cantor-Curtis classification  were finally included. Pathak et al. .Prosthetic RehabilitationImplant-supported prosthesis (ISP) was adopted in four studies -10. ThirPost-prosthetic Follow-UpPost-prosthetic recall visits were reported in 48 cases with duration ranging between 48 hours to four years . Recall visit without duration was noted in eight cases, while follow-up was not reported among 10 cases.DiscussionProsthodontic rehabilitation of hemimandibulectomy defects is a challenging task including multiple procedures with an interdisciplinary approach towards restoring function and patient satisfaction.Prosthetic Alternatives According to Nature and Extent of the DefectProsthetic rehabilitation post-resection with radiotherapy poses challenges in implant placement due to the risk of radiation-induced osteoradionecrosis at the bone level. Dental implant placement within 12 months following radiotherapy corresponds with a 34% increased risk of failure and thus recommends placement after one year of irradiation. Radiation dose greater than 5,000 cGy increases the implant failure rate to 33% . A case Segmental mandibulectomy distal to the canine (CC class II) devoid of hard tissue reconstruction leaves the patient with no option of chewing due to mandibular deviation towards the resected side exhibiting rotation and angular path of jaw closure. This is aggravated in edentate arches due to the generation of unilateral occlusal forces during mastication resulting in dislodgement of maxillary denture; therefore, implant-supported overdenture adjunct with MRP has overcome these problems and has proved to be a boon in completely edentate patients. The abnormal jaw relations along with the angular path of closure favoured the use of monoplane teeth adjunct with neutrocentric concept are advisable to achieve a non-restrictive occlusion. The maxillary ramp stabilizes the prosthesis, limits the mandibular deviation and provides a broad occlusal table for ease in mastication ,9. If imA class III segmental resection extending to the midline elicits increased mandibular deviation with marked facial disfigurement, decreased masticatory function, diminished speech and altered occlusion with condylar rotation resulting in anterior open bite thereby complicating the treatment prognosis. Early initiation of post-resection jaw exercises helps to loosen the scar contracture and improve the maxillomandibular relationship . IntermaClass IV defect involves the resection of the lateral aspect of the mandible augmented to maintain pseudo articulation of bone and soft tissue in the region of the ascending ramus. It presents with facial asymmetry, mandibular deviation and improper occlusion due to the depressor muscle action of the normal side. Various treatment modalities have been proposed to reduce post-surgical mandibular deviation . A similar challenging situation with limited interarch distance causing occlusal interference by the buccal flange of MGFP on the non-resected side has been resolved by using an MRP in relation to the non-defect side and MGFP on the defect side to establish bilateral guidance. This unique combination of prosthesis reduces the deviation and retrains the individual to achieve proper occlusion by neuromuscular reprogramming activity .Authors have suggested immediate mandibular reconstruction post-resection with vascularised free flaps to prevent implant placement complications post-radiotherapy  and imprThe present systematic review had limitations as it was restricted only to Medline by means of PubMed and Google Scholar; so, the literature published on other databases and languages apart from English may have been omitted despite meeting all our inclusion criteria. Randomised controlled trials (RCT) were scarce in our search on the particular topic; therefore, the next level of evidence (i.e. case reports and series) was included; therefore, authors are urged to perform extensive RCTs on similar topics. Post-prosthesis observation duration was a deficit in many studies, while few reported short-term (less than one month) follow-up; therefore, future studies with long-term follow-up data are recommended for assessing the prosthesis longevity. Several data were lacking from the reviewed literature including CC classification, reconstruction type and scarring which decreases the article quality limiting us to deduce a strong correlation among the type of prosthesis to be selected for a particular situation.This study suggests that the first line of treatment following surgical resection includes hard tissue graft reconstruction along with interim guidance and definitive implant prosthesis (after one year in case of irradiation). In cases of CC classes II, III, IV and V defects associated with mandibular deviation where occlusion can be attained manually, MGFP/MRP would be the preferred treatment modality followed by a definitive prosthesis. In mandibular-deviated cases where occlusion is not achievable manually, a twin occlusion prosthesis is recommended.In spite of certain limitations, this review highlights numerous prosthetic approaches according to the extent of hemimandibular defect and type of reconstruction in a snapshot. Adequate patient adaptation with minor complications in few which when corrected revealed favourable results. This study will guide the clinicians during treatment planning of hemimandibulectomy patients.


Immunotherapy is a promising strategy for triple-negative breast cancer (TNBC) patients, however, the overall survival (OS) of 5-years is still not satisfactory. Hence, developing more valuable prognostic signature is urgently needed for clinical practice. This study established and verified an effective risk model based on machine learning methods through a series of publicly available datasets. Furthermore, the correlation between risk signature and chemotherapy drug sensitivity were also performed. The findings showed that comprehensive immune typing is highly effective and accurate in assessing prognosis of TNBC patients. Analysis showed that IL18R1, BTN3A1, CD160, CD226, IL12B, GNLY and PDCD1LG2 are key genes that may affect immune typing of TNBC patients. The risk signature plays a robust ability in prognosis prediction compared with other clinicopathological features in TNBC patients. In addition, the effect of our constructed risk model on immunotherapy response was superior to TIDE results. Finally, high-risk groups were more sensitive to MR-1220, GSK2110183 and temsirolimus, indicating that risk characteristics could predict drug sensitivity in TNBC patients to a certain extent. This study proposes an immunophenotype-based risk assessment model that provides a more accurate prognostic assessment tool for patients with TNBC and also predicts new potential compounds by performing machine learning algorithms. Breast cancer is one of the most common cancers among women worldwide, which has different pathological and molecular subtypes including luminal A, luminal B, human epidermal growth factor receptor overexpression (HER-2+) and triple-negative breast cancer (TNBC) . TNBC isImmunotherapy for cancer is often based on the cancer immune cycle theory, which includes the enhancement of stimulatory immune factors and immune checkpoint inhibitors (ICIs) . The sucTherefore, establishing reliable predictive biomarkers to identify subgroups that may benefit for TNBC is urgently needed. Besides, adopting comprehensive evaluation of tumor immunophenotype-based treatment strategies for each patient through cancer immune-cycle and immune cell infiltration status are essential to promote the development of effective immunotherapies. In the current study, publicly accessible data of TNBC were retrieved from The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database to establish the robust signature through a series of bioinformatics methods. By combining multi-gene expression datasets, we developed and validated the risk model based on cancer immunophenotypes, and explored its performance in predicting prognosis. In addition, we comprehensively investigated the association between this signature with immune-related characteristics, immunotherapy response and drug sensitivity in TNBC patients. Our results suggested that this cancer immunophenotype-based signature could be used as a promising biomarker in predicting clinical outcome and immunotherapy response in TNBC patients.https://www.ncbi.nlm.nih.gov/geo) and was selected as the training cohort on account of the optimal sample size. Another three microarray datasets named GSE16446 (107 samples), GSE20685 (225 samples) and GSE20711 (78 samples) were also obtained from GEO database and used as the validation cohorts, in addition, TNBC RNA sequencing datasets which downloaded separately from The Cancer Genome Atlas  and Molecular Taxonomy of Breast Cancer International Consortium  were used as another two validation cohorts, respectively. Summary information of above cohorts was listed in 2 transformed.This research was conducted on publicly available database through a series of bioinformatics methods. Transcript profiles and corresponding clinical information of six cohorts containing a total of 694 TNBC patients were acquired to construct and validate the risk signature after removing the samples with unknown survival time and outcome. Details were as follows: microarray dataset GSE103091 (107 samples) was downloaded from Gene Expression Omnibus  is a webtool that can assess the immune microenvironment on the base of the cancer-immunity cycle (The Tumor Immunophenotype (TIP) database  and employed to quantify the pathways through “ssGSEA” method. The infiltration level of 10 immune cells were also quantified through “MCPcounter” algorithm. Next, we also calculated the relative infiltration level of 22 kinds of immune cells by CIBERSORT method. Then, the characteristic genes of 28 immune cells which obtained from previous study , and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. The Gene set enrichment analysis (GSEA) was then conducted to further analysis the difference of biological functions between different groups based on the Hallmark gene set through “clusterProfiler” package.Differential expression analysis between diverse molecular subtypes and risk models were conducted by “limma” package and visualized through volcano plot. The selection criterion was |logTo develop a consensus model with high accuracy and stable performance, we integrated 10 machine learning algorithms and 101 algorithm combinations. The integrative algorithms included random survival forest (RSF), elastic network (Enet), Lasso, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalised boosted regression modelling (GBM), and survival support vector machine . The signature generation procedure was as follows: (a) Firstly, univariate Cox regression was employed to identify the prognostic genes in training cohort; (b) Then, 101 algorithm combinations were performed on the prognostic genes to fit prediction models based on the leave-one-out cross-validation (LOOCV) framework in the training cohort; (c) All models were detected in five validation datasets ; (d) For each model, the Harrell’s concordance index (C-index) was calculated across all validation datasets, and the model with the highest average C-index was considered optimal. The risk score was calculated as following formula: The “maftool” package was used to explore the somatic mutations in TCGA-TNBC patients and the top 10 mutated genes were presented in waterfall plot. Besides, the copy number variation (CNV) data of TCGA-TNBC was also downloaded and used to display the proportion of deletion and amplification of genes according to the risk model.http://tide.dfci.harvard.edu/) for immune treatment effect evaluation. The tAUC of risk signature and TIDE score were analyzed in the three cohorts and the comparison between these two indicators were also performed to distinguish the sensitivity and specificity to immunotherapy response.In order to explore the superiority of the risk signature, the time-dependent area under curves (tAUC) of the signature and other clinicopathological features were analyzed and compared in METABRIC cohort and TCGA-TNBC cohort, respectively. Then, the TIDE score of three immunotherapy cohorts  were calculated through the online tool  as a training set. The drug sensitivity of each patient in the GSE103091 cohort was predicted by CTRP and PRISM methods. Then screening for potential regulation of drugs through the setting as |cor| > 0.3.To further investigate potential therapeutic target drugs in the high-/low-risk group, we used the drug-sensitive cell lines in the CCLE database  and GraphPad Prism 8.0  were used for all statistical analysis and visualization. Univariate Cox regression analysis was performed to evaluate the significant prognostic genes. Quantitative data were compared between different groups through Wilcoxon rank sum test. Relationships between risk scores and expression levels of different genes were examined by Spearman’s correlation analysis. Unless otherwise specified, p < 0.05 was considered as statistically significant.R software (The workflow of our research was presented in p < 0.05). Similar results were observed in GSE20685 cohort (p < 0.05). Then, PCA analysis was conducted based on Neutrophils marker genes, and PCA dimension reduction distributions of the two subtypes were shown in A total of 166 marker genes were collected in the seven stages of the cancer-immunity cycle, including checkpoints, cytotoxic factors, chemokines, and major histocompatibility complex (MHC) molecules . The CDFAbove results indicated that the patients in C2 showed a better prognosis than the patients in C1. Next, the study explored the differences in immunity between these two clusters. Obviously, C2 displayed a higher immune score, stomal score, and ESTIMATE score compared with the C1 . In addiIn order to further investigate and verify the differences of biological functions between the two clusters, the differential analysis was performed to complete this task. The heatmap was showed in p < 0.05, among which were all protective factors. Next, these 30 genes were used to develop a consistent prognostic model through our integrated program based on machine learning approach. In brief, 101 prediction models were filtered through the LOOCV framework, meanwhile, the C-index for each model was also calculated in the training cohort and validation cohorts to select the most outstanding candidate. Interestingly, the optimal model was a combination of CoxBoost and RSF, with the highest average C-index equal to 0.622 , indicating that the low-risk group which evaluated based on our model had a higher immune infiltration. We further analyzed the differences in the distribution of somatic immune cells between the two subpopulations and found significant differences in T cells and three types of macrophages in the low-risk group .Comprehensive immune subtyping was developed by immunization scores. The results showed significant prognostic differences between the high and low immune groups. GSEA analysis was performed to explore possible signaling pathways associated with diverse risk groups. These signaling pathways have not been experimentally verified, and further studies are needed to explore the specific mechanisms influencing immune scores in TNBC patients. We further verified the risk model constructed by machine learning. The results showed that a strong negative correlation was found between risk score and immune score, indicating that the low-risk group had higher immune infiltration. Compared with other clinicopathological features, risk score showed strong survival prediction ability, which was very effective and accurate in evaluating the prognosis of TNBC patients. This study suggested the possibility of immunotyping for clinical therapeutic efficacy monitoring, so more TNBC immunotyping data are needed to further support future clinical treatment. But there were several study limitations. Although the evaluation and validation of this risk model across multiple datasets, it remains essential to conduct a large-scale, multicenter, prospective study to authenticate our discoveries. In the time ahead, a series of investigations should be carried out to authenticate the risk model Considering the application prospect of this model, we further studied the potential therapeutic target drugs in the high-/low-risk group, and screened the potential regulatory drugs according to the drug sensitivity of patients in the data set. New agents and new combinations of immunotherapies may unlock the key to truly personalized cancer medicine. Specifically, efforts focused on understanding biology, biomarker selection, and strategies to enhance immunotherapy response are vital to the success of immunotherapy in TNBC and other cancers in general.This study proposed an immunophenotype-based risk assessment model that provide a more accurate prognostic prediction ability for TNBC patients by machine learning algorithms. Meanwhile, new potential compounds which may influence the chemotherapy response were also performed. The disadvantage of this study is that the drug-related conclusions obtained from our research have not been clinically proven at present, and further analysis is still needed to support the study results.


Immunotherapy using systemic immune checkpoint inhibitors (ICI) and chimeric antigen receptor (CAR) T cells has revolutionized cancer treatment, but it only benefits a subset of patients. Systemic immunotherapies cause severe autoimmune toxicities and cytokine storms. Immune-related adverse events (irAEs) plus the immunosuppressive tumor microenvironment (TME) have been linked to the inefficacy of systemic immunotherapy. Intratumoral immunotherapy that increases immunotherapeutic agent bioavailability inside tumors could enhance the efficacy of immunotherapies and reduce systemic toxicities. In preclinical and clinical studies, intratumoral administration of immunostimulatory agents from small molecules to xenogeneic cells has demonstrated antitumor effects not only on the injected tumors but also against noninjected lesions. Herein, we review and discuss the results of these approaches in preclinical models and clinical trials to build the landscape of intratumoral immunotherapeutic agents and we describe how they stimulate the body’s immune system to trigger antitumor immunity as well as the challenges in clinical practice. Systemic and intratumoral combination immunotherapy would make the best use of the body’s immune system to treat cancers. Combining precision medicine and immunotherapy in cancer treatment would treat both the mutated targets in tumors and the weakened body’s immune system simultaneously, exerting maximum effects of the medical intervention. Cancer is a major public health problem worldwide, which causes approximately 10 million deaths each year, mostly due to advanced cancers when tumors metastasize or are inoperable with surgery. At this stage, patients have mostly been treated with conventional therapies such as surgery, radiotherapy, chemotherapy and targeted therapy as well as the combination of these treatments, but eventually, all these treatments fail, and patients die. Because these modalities use physical or chemical methods to treat tumors cells, either the physical forces are too strong and damage normal tissues or the chemical strength is too toxic for normal cells, and the body eventually succumbs to cancer and dies. Cancer deaths result from infection, organ failure, infarction, hemorrhage and cardiovascular diseases because of failing bodies . The bodConventional therapies like surgery, radiotherapy, chemotherapy and targeted therapy all target tumors to treat cancer. Cancer interacts with the body’s immune system that involves multiple factors, diverse cell lineages and the interactions among them. To stimulate antitumor immunity, proper immune responses need to be driven out to enhance tumor eradication by the body’s immune system. The understanding of immune system control in tumor growth and development has increased significantly in recent years, resulting in the development of cancer immunotherapy that has changed the outcomes and extended the survival of advanced cancer patients. This recent re-emergence of immunotherapy has made a breakthrough in saving cancer patients’ lives by targeting the body’s immune system to treat cancer. Cancer immunotherapy harnesses the body’s immune system to treat cancers by eliciting the innate and adaptive immune responses to eradicate tumor cells; by using the body’s own immune system to fight off cancer cells, the target is the body’s immune system rather than the tumor cells. Among cancer immunotherapy approaches, one approach is to increase antitumor T-cell responses, either by blocking inhibitory pathways with immune checkpoint inhibitors (ICIs) or by targeting stimulatory pathways like chimeric antigen receptor T cells . ICIs ar10 cells intravenously; despite intensive medical intervention, after 5 days treatment, the patient still passed away [All of the above treatments are administered intravenously and rely on systemic biodistribution to deliver the therapeutic agents into tumors in order to exert their effects, which could be hindered by the immunosuppressive tumor microenvironment (TME). The TME includes stromal cells and immune cells such as natural killer (NK) cells, T cells and a variety of myeloid cells, including myeloid-derived suppressor cells (MDSCs), dendritic cells (DCs) and macrophages, and surrounding tumor cells as well as the extracellular matrix scaffolds supporting the cells. The immunosuppressive cold tumor microenvironment lacks infiltrating antitumor T cells to eradicate tumor cells, but fills with MDSCs, M2-polarized macrophages and regulatory CD4+ T cells (Treg) as well as immunosuppressive cytokines like TGF-β, and is linked to the failure of systemic immunotherapy . In addiTo maximize the benefits of cancer immunotherapy and to lessen systemic toxicities, in recent years, locoregional intratumoral immunotherapy has been studied to complement the deficiency of systemic immunotherapy. However, a tumor-directed approach to induce immune responses to attack tumors had been investigated more than a century ago by Dr. William B. Coley, regarded as the father of cancer immunotherapy. Considered to be the origin of modern cancer immunotherapy, by injecting bacteria toxins (a cocktail of heat-killed Streptococcus pyogenes and Serratia marcescens) intratumorally with the purpose to develop erysipelas to stimulate the immune system, Dr. Coley demonstrated the efficacy of this treatment in patients with osteosarcoma ,12. IntrSalmonella typhimurium); and cells .The therapeutic principle of intratumoral immunotherapy is to elicit local immune responses to generate both local and systemic antitumor effects to eradicate all tumor cells in the body, bringing durable clinical benefits. There are different classes of agents that can potentially elicit various antitumor immune responses inside the body to react to the broad tumor heterogeneity . From smAs the essential components of the body’s innate immune system, the mammalian Toll-like receptors (TLRs) are germline-encoded receptors expressed in cells as sensors to respond to various distinct structural moieties present in microbes known as pathogen-associated molecular patterns (PAMPs) or endogenous damage-associated molecular patterns (DAMPs) in injured cells. PAMP/DAMP-TLR bindings elicit the secretion of proinflammatory cytokines and the subsequent differentiation and maturation of antigen-presenting cells (APCs) that target invading microbes or damaged cells with adaptive immune responses. Different types of TLRs react to particular PAMPs in order to recognize and respond to invading microbial pathogens. Lipoteichoic acid and peptidoglycan of bacterial components stimulate TLR2/TLR6 heterodimer, double-stranded RNAs from viruses activate TLR3, lipopolysaccharides (LPS) from Gram-negative bacteria stimulate TLR4, guanosine- or uridine-rich single-stranded RNAs from viruses activate TLR7/8 and viral DNAs rich in unmethylated CpG motifs stimulate TLR9 . In addiImiquimod is a small-molecule immunomodulatory compound that exerts its biological efficacy through agonistic stimulation of TLR7 that recognizes single-strand RNAs (ssRNAs) with viral features in immune cells. In a murine mesothelioma model, locally delivered imiquimod in combination with systemic anti-CD40 immunotherapy not only significantly enhanced the local antitumor response with 30% complete resolution, but also inhibited distal tumor growth . The intCpG oligodeoxynucleotides are TLR9 agonists. Intratumoral CpG oligodeoxynucleotide treatment with local low-dose radiotherapy in patients with low-grade B-cell lymphoma induced local and systemic antilymphoma clinical responses at the irradiated tumor site, regression of distant, nonirradiated sites of lymphoma in an immune-mediated response manner with a shorter time to disease progression and was well tolerated . IntratuOther macromolecules like glycolipids with α-gal  epitopes  have also been explored as an intratumoral immunotherapeutic agent. After intratumoral α-gal glycolipid injection, α-gal glycolipids insert into tumor cell membranes and α-gal is bound by anti-Gal, the most abundant natural antibody in humans, constituting 1% of immunoglobulins; the binding activates complements and cleavage peptides C5a and C3a, and then recruits inflammatory cells and APCs into the treated lesion, converting tumor cells into an autologous tumor-associated antigen vaccine. A preclinical study has demonstrated intratumoral α-gal glycolipids treatment could prevent the development of metastases at distant sites and could defend against tumor rechallenge in treated mice . IntratuPeptides such as hormones consist of short chains of amino acids and proteins like cytokines, and antibodies (Abs) are macromolecules made up of multiple peptide subunits. Both are naturally occurring large molecules encoded by multiple genes in the body to perform different physiologic functions from metabolism to immunity. Peptides have been used as drugs because of their functions as hormones and neurotransmitters or in antimicrobial activities ,75. CytoOncolytic peptides have been studied for their actions as intratumoral immunotherapies. The oncolytic peptide LTX-315 (ruxotemitide) was designed based on the structure of bovine lactoferricin and has been studied as an intratumoral treatment in a mouse B16 melanoma model. Intratumoral administration of LTX-315 has been shown to cause complete tumor regression in the majority of mice and the recruitment of immune cells into the tumor parenchyma was found in LTX-315-treated mice . A phaseRecombinant cytokines have been applied in clinics to treat cancers, such as IFN alpha for follicular lymphoma, hairy cell leukemia and melanoma  and IL-2Granulocyte-macrophage colony-stimulating factor (GM-CSF) induces DC cell development and maturation as well as T-cell proliferation and activation to elicit innate and acquired immunity. A phase I clinical trial reported that intralesional injections of GM-CSF induced regression of subcutaneous metastases in melanoma patients with increased Langerhans cells and T-cell infiltration at injection sites, which correlated with clinical responses . The comMost currently approved therapeutic Abs are antagonist that either directly bind to surface markers expressed on cancer cells like HER2, growth factors that support tumor growth like vascular endothelial growth factor, or regulatory proteins on cells like CTLA-4, PD-1 and PD-L1. Intratumoral injection of anti-CTLA4 antibodies in a slow-release formulation with an eight-fold lower dose of antibodies achieved tumor eradication as systemic delivery and resulted in thousand-fold decreased levels of antibodies in the serum, reducing adverse events and the risk of autoimmunity . IntratuAntibody–drug conjugates (ADCs) are a new class of therapeutic Abs, which are composed of a monoclonal antibody that uses a chemical linker to conjugate a cytotoxic payload, allowing cytotoxic agents directed toward cells with a target antigen to be expressed on the cancer cell surface and bound by the Ab to reduce systemic exposure of cytotoxic agents . The folDue to advancements in genetic engineering technology, genes encoding cytokines can be inserted into DNA plasmids, mRNAs or viruses and locally injected into tumors, allowing cytokines to be durably expressed in situ compared to intratumoral injections of cytokine proteins. A phase II clinical trial was conducted in patients with stage III/IV melanoma by intratumorally delivering DNA plasmids encoding IL-12 (tavokinogene telseplasmid) into tumors using electroporation, and the results showed that the treatment was well tolerated and led to systemic immune responses, tumor regression and increased immune infiltration even in untreated/distal lesions, but adaptive immune resistance limited the response . IntratuWhen transfected into cells, synthetic mRNAs can be used as templates for the synthesis of designed proteins and peptides, whose properties make mRNAs encoding gene products a novel therapeutic modality. With nucleoside modification and elimination of double-stranded RNA plus the use of nanoparticle-based formulations, the immunomodulatory activity of mRNAs has been reduced and the transfection efficiency of mRNAs has been increased, thus resulting in increased and prolonged expression of designed products such as cytokines, costimulatory receptors or therapeutic antibodies . In precSpontaneous tumor regression has been recorded after viral or bacterial infections, which has provided the basis for clinical trials to use viruses or bacteria as therapeutic agents to treat cancer patients. Selected and genetically modified viruses with the capability to specifically infect and propagate in tumor cells have been used to destroy tumor cells and to stimulate the innate immune response as well as adaptive antitumor immunity to eradicate distant uninfected tumor cells, called oncolytic virotherapy ,93.Talimogene laherparepvec (T-VEC) is a herpes simplex virus 1, genetically modified to express GM-CSF and selectively infect cancer cells. So far, T-VEC is the only approved viral cancer immunotherapy for local treatment of unresectable cutaneous, subcutaneous, and nodal lesions in patients with melanoma recurrent after initial surgery . In the Other oncolytic viruses are also being studied in preclinical studies and clinical trials to identify more intratumoral virotherapy options. CAVATAK, an oncolytic immunotherapy, is a bioselected oncolytic strain of coxsackievirus A21 (CVA21) that preferentially infects intercellular adhesion molecule 1 expressing tumor cells. Intratumoral injection of CAVATAK to patients with advanced melanoma, in the phase II CALM study, increased immune cell infiltration and immune-related response genes in tumors . CanerpaIntratumoral HF10 plus ipilimumab combination treatment tested in a phase II trial in stage IIIB, IIIC or IV unresectable ipilimumab naïve melanomas has demonstrated a favorable benefit/risk profile and encouraging antitumor activity in patients with stage IIIB, IIIC or IV unresectable or metastatic melanoma . Direct Pexa-Vec  is an oncolytic vaccinia virus with the thymidine kinase gene inactivated and genetically modified to express human GM-CSF for selective replication in cancer cells, with cell-cycle defects and epidermal growth factor receptor/Ras pathway activation to cause direct oncolysis once infecting cancer cells, plus its GM-CSF expression that also stimulates shutdown of tumor vasculature and antitumor immunity . IntratuTasadenoturev (DNX-2401) is an oncolytic replication-competent adenovirus with a 24 bp deletion in the E1A region of the genome, enabling the virus to replicate in cancer cells selectively and efficiently. In a preclinical study of a mouse GL261-glioma model, intratumoral DNX-2401 induced cytotoxic effects in mouse glioma cells. Viral treatment in GL261-glioma-bearing mice increased the infiltration of innate and adaptive immune cells into tumors, stimulating Th1 immunity at the tumor site, which elicited specific anti-glioma immunity . AdditioReolysin (pelareorep) is a human reovirus type 3 strain with direct oncolytic activity in Ras-activated tumor cells and the ability to elicit dendritic cell maturation as well as NK cell activation and recruitment. The combination of local intratumorally delivered reovirus with systemic immune checkpoint inhibition in a murine subcutaneous B16 melanoma model increased survival compared to untreated and monotherapy groups by increasing NK killing of virus-infected cells and inhibited immune suppression caused by Foxp3+ Treg cells . A phaseThe use of bacteria to target tumors has been explored as a unique therapeutic option since Dr. Coley’s efforts to solve the ongoing challenges of cancer treatment. However, until now, only Bacillus Calmette–Guérin (BCG), a strain of Mycobacterium bovi, has been approved only for the intravesical treatment of non-muscle invasive bladder cancer (NMIBC) to prevent recurrence , althougSalmonella typhimurium is a promising cancer immunotherapy option because it can be modified and proliferates both in non-hypoxic and hypoxic tumor sites. Intratumoral delivery of recombinant attenuated Salmonella enterica serovar typhimurium vaccine significantly repressed Her-2/neu-expressing tumor growth by causing transformation of immunosuppressive MDSCs into TNF-α-secreting neutrophils and decreasing the generation of Treg cells, especially in the presence of tumor-specific CTLs [Salmonella typhimurium, designated VNP20009, generated by deletion of the msbB and purL genes, was tested in a phase I trial and showed a safe profile [S. typhimurium induced tumor cell apoptosis, decreased tumor angiogenesis and inhibited the growth of the injected schwannoma tumors in two murine schwannoma models; adding anti-PD-1 antibodies to the intratumoral injection of S. typhimurium had an additive effect on suppressing schwannoma growth [fic CTLs . Intratu profile . Intratua growth . Anothera growth . One inja growth .Clostridium novyi is a highly mobile bacterium and a spore-forming organism that is extremely sensitive to oxygen. Intratumoral injection of Clostridium novyi  spores, an attenuated strain of Clostridium novyi, has been performed in companion dogs bearing spontaneous solid tumors with an objective response rate of 37.5%  [C. novyi-NT spores, and this treatment reduced the tumor within and surrounding the bone [C. novyi-NT showed that among 22 evaluable patients, 9 patients (41%) had a decrease in the size of the injected tumor and 19 patients (86%) had stable disease as the best overall response in injected and noninjected lesions combined [C. novyi-NT to treat patients with injectable, treatment-refractory solid tumors in combination with intravenous pembrolizumab demonstrated a manageable toxicity profile of intratumoral C. novyi-NT plus systemic pembrolizumab, which resulted in partial responses in two of nine patients  [sponses) . This cathe bone . A firstcombined . A furth cancer) .Human immune cells including DC and T cells can be isolated from patients (autologous) or donors  for ex vivo expansion and further modification with stimulating factors or genetic engineering to produce clinical-grade cell therapies. For example, Sipuleucel-T, which has been approved for prostate cancer, consists of autologous peripheral-blood mononuclear cells, including APCs, that have been stimulated ex vivo with a recombinant fusion protein (PA2024), which is a prostate antigen, prostatic acid phosphatase fused to GM-CSF, an immune cell activator . The othThe adoptive T-cell therapy using TILs or CAR-T cells has demonstrated clinical benefits for patients with melanoma (TILs) and patients with hematological malignancies (CAR-T cells) through the systemic intravenous infusion of ex vivo expanded TILs or genetically modified T cells, however, with the challenges of T-cell exhaustion and an immunosuppressive tumor microenvironment . IntratuXenogeneic cell  therapy has been proposed to treat tissue failures and degenerative diseases such as type I diabetes and Parkinson’s disease through cell replacement to restore the function of tissue failures; however, so far, there is no approval for xenogeneic cell therapy, because the body’s immune responses to xenogeneic cells are powerful and multifaceted, involving innate immune components and adaptive immunity of T cells and antibodies to reject the cells . HoweverOral, intramuscular and intravenous medication administration routes have been applied routinely; however, in clinics, intratumoral injection is a new way to deliver drugs to the body. Currently, there is only one FDA-approved indication with the intratumoral approach for patients with melanoma; others are still in clinical trials and preclinical studies. There are several gaps and/or needs that require more studies on intratumoral immunotherapeutic agents. Several key issues need to be resolved before intratumoral immunotherapy could fulfill its promise.Intratumoral injection has not been used in routine clinical practice. The safety of this emerging procedure is a prime concern in identifying lesions that are safely accessible and injected without causing tumor spread and hemorrhage. As mentioned previously, T-VEC, the first and only FDA-approved oncolytic viral therapy for the local treatment of unresectable cutaneous, subcutaneous, and nodal lesions in patients with melanoma recurrent after initial surgery, has been designed to be injected into nodal tumor lesions that are visible, palpable or detectable by ultrasound guidance . Thus, i6 plaque-forming units (PFU) per mL and, after 3 weeks, second and subsequent doses are administered up to 4 mL at a concentration of 108 PFU per mL every two weeks for at least 6 months, unless other treatment is required or until there are no injectable lesions to treat [9 or 108 PFU using a multi-pronged Quadrafuse needle was administered to ensure even distribution of the virus throughout the tumor when possible, into up to five intrahepatic tumors at different time points, i.e., days 1, 15 and 29, with the volume of JX-594 solution to be injected proportionally to the volume of the tumor to be injected (25% of the tumor volume) [The ideal intratumoral immunotherapy regimen (dosage and timing of injection) for each immunostimulatory agent that gives the optimal antitumor response has to be determined depending on their mechanisms of action and local PK/PD properties. The injection volume could be determined by the tumor size, but the drug amount  would vary among different therapeutic classes, similar to the treatment schedule. For T-VEC, the starting initial dose is up to a maximum of 4 mL at a concentration of 10to treat . In a cl volume) . The regIn conventional pharmacology for oral or intravenous routes, there are four pharmacokinetic characteristics of a drug that need to be effectively determined, i.e., absorption, distribution, metabolism and excretion (ADME), which describe how a drug moves and transforms in the body, causing various drug level exposures within tissues. However, intratumoral administration directly injects the drug into the effect sites, where a high initial tissue concentration occurs, and then the drug diffuses throughout the injected sites and into the systemic circulation over time. The pharmacokinetics of locally injected agents are not easily defined as systemic administration. The pharmacokinetic framework for intratumorally injected agents would focus on local tissue/tumor exposure and retention to correlate with therapeutic efficacy and exert maximal therapeutic effect . Due to The identification of biomarkers to correlate local activity with systemic efficacy is essential in intratumoral immunotherapy pharmacodynamic studies, which could include cytokines, antibodies and immune cells . The immTo measure the outcome of intratumoral immunotherapy, the objective response rate of tumor responses has been measured by radiological assessment of injected and noninjected sites using the intratumoral immunotherapy-specific Response Criteria for Intratumoral Immunotherapy in Solid Tumors (itRECIST), which is based on regression of both injected and noninjected lesions . By applIntratumoral immunotherapy provides several advantages over conventional systemic cancer immunotherapy strategies by maximizing the therapeutic index values of different immunotherapeutic agents and reducing systemic exposure. In addition, as a local treatment and targeting the body’s immune system therapy, intratumoral immunotherapy offers a therapeutic option to combine with systemic chemo- and targeted therapies as well as regional surgery and radiotherapy to form a comprehensive cancer treatment. With all the therapeutic options  available to patients as well as the advancement of precision medicine to find targets and immunotherapy to strengthen the body’s immune system, it allows a combination treatment plan that selects these treatment options to comprehensively manage cancer patients. Complete cancer management that follows the dogma of Eastern medicine, i.e., “to cure the diseases and let the body return to a healthy state, medicines need to treat the causes of diseases and treat the body that responds to the diseases” would transform cancer management to bring best outcomes to patients.


However, the translation of computational results into a clinical use-case scenario remains challenging. A rate-limiting step is the rigorous selection of reliable biomarker candidates among a host of biological features included in multivariate models. We propose Stabl, a machine learning framework that unifies the biomarker discovery process with multivariate predictive modeling of clinical outcomes by selecting a sparse and reliable set of biomarkers. Evaluation of Stabl on synthetic datasets and four independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used SRMs at similar predictive performance. Stabl readily extends to double- and triple-omics integration tasks and identifies a sparser and more reliable set of biomarkers than those selected by state-of-the-art early- and late-fusion SRMs, thereby facilitating the biological interpretation and clinical translation of complex multi-omic predictive models. The complete package for Stabl is available online at  Omic technologies have also dictated a shift in statistical analysis of biological data. The traditional univariate statistical framework is maladapted to large omic datasets characterized by a high number of molecular features p relative to the number of samples n. The p ≫ n scenario drastically reduces the statistical power of univariate analyses, a problem that cannot be easily overcome by increasing the value of n due to cost or sample availability constraints.4High-content omic technologies, such as transcriptomics, metabolomics, or cytometric immunoassays, are increasingly employed in biomarker discovery studies.p ≫ n problem: 1) prediction of the clinical endpoint via identification of a multivariate model with high predictive performance (predictivity), 2) selection of a limited number of features as candidate clinical biomarkers (sparsity), and 3) confidence that the selected features are among the true set of features .Statistical analysis in biomarker discovery research comprises three related yet distinct tasks, all of which are necessary for translation into clinical use and impacted by the 5 or elastic net (EN),6 provide predictive modeling frameworks adapted to p ≫ n omic datasets, but the selection of a sparse and reliable set of candidate biomarkers remains an important challenge. Most rely on an L1-regularization to limit the number of features used in the final model. However, the learning phase of the model is often performed on a limited number of samples, such that small perturbations in the training data can yield wide differences in the features selected in the predictive model.9 This undermines confidence in the features selected, as current SRMs do not provide objective metrics to determine whether these features are truly related to the outcome. This inherent limitation of SRMs can result in poor sparsity and reliability, thereby hindering the biological interpretation and clinical significance of the predictive model. As such, few omic biomarker discovery studies progress to further clinical development phases.11Several machine-learning methods, including sparsity-promoting regularization methods (SRMs), such as least absolute shrinkage and selection operator (Lasso)13 However, in these methods, the threshold for feature selection or the target false discovery rate (FDR) are defined a priori, which uncouples the feature selection from the multivariate modeling process. Without prior knowledge on the data, these methods can lead to suboptimal feature selection, requiring multiple iterations to identify a desired threshold. This limitation also precludes optimal integration of multiple omic datasets into a unique predictive model, as a single fixed selection threshold may not be suited to the specificities of each dataset.High-dimensional feature selection methods such as stability selection (SS) or Model-X knockoff improve reliability by controlling for false discoveries in the selected set of features.Here we introduce Stabl, a supervised machine learning framework that bridges the gap between multivariate predictive modeling of high-dimensional omic data and the sparsity and reliability requirements of an effective biomarker discovery process. Stabl combines the injection of knockoff-modeled noise or random permutations into the original data, a data-driven signal-to-noise threshold, and integration of selected features into a predictive model. Systematic benchmarking of Stabl against Lasso, EN, and SS using synthetic datasets, three existing real-world omic datasets, and a newly generated multi-omic clinical dataset demonstrates that Stabl overcomes the shortcomings of state-of-the-art SRMs: Stabl yields highly reliable and sparse predictive models while identifying biologically plausible features amenable to further development into diagnostic or prognostic precision medicine assays.https://github.com/gregbellan/Stabl.The complete package for Stabl is available online at 12 However, as uninformative features typically outnumber informative features in high-dimensional omic datasets,11 the fit of an SRM model on a single cohort can lead to selection of many uninformative features despite a low probability of selection.14 To address this issue, Stabl implements the following strategy  on subsamples of the data using a procedure similar to SS.3 or knockoff sampling,16 which we assume behave similarly to uninformative features in the original dataset17 . The artificial features are used to construct a surrogate of the false discovery proportion (FDP+). We define the “reliability threshold”, θ, as the frequency threshold yielding the minimum FDP+ across all possible thresholds. This method for determining θ is objective, in that it minimizes a proxy for the FDP. It is also data-driven, as it is tailored to individual omic datasets.To define the optimal frequency threshold, Stabl creates artificial features unrelated to the outcome (noise injection) via random permutationsWhen applied to a single cohort drawn at random from the population, SRMs will select informative features  with a higher probability, on average, than uninformative features .As a result, Stabl provides a unifying procedure that selects features above the reliability threshold while building a multivariate predictive model. Stabl is amenable to classification and regression tasks and extends to integration of multiple datasets of different dimensions and from different omic modalities.n), total features (p), and informative features (S). Models were evaluated using three performance metrics .Predictivity: mean square error (MSE).We benchmarked Stabl against Lasso and EN using synthetically generated training and validation datasets containing known informative and uninformative features . Simulat metrics :Sparsit+ defined by Stabl experimentally controls the FDR at the reliability threshold θ, as the true value of the FDR is known for the synthetic dataset. We observed that FDP+ (θ) was indeed greater than the true FDR value (+ (θ), thus providing a theoretical validation of our experimental observations .Before performing benchmark comparisons, we tested whether the FDPDR value  and S1. Stabl was tested using a random permutation method  and S2–5θ while building the multivariate model compared to SS . Three representative frequency thresholds were evaluated: 30%, 50%, or 80% (n<75), the 30% threshold had the best sparsity and reliability. However, for a large sample size (n>500), the 80% threshold resulted in greater performances. In contrast, Stabl models systematically reached optimal sparsity, reliability, and predictivity performances. Further, we show that θ varied greatly with the sample size , high-content proteomics (SomaLogic and Olink platforms), untargeted metabolomics, and single-cell mass cytometry.Sparsity: determined by the average number of features selected throughout the CV procedure.Reliability: assessed using univariate statistics in the absence of a known true set of features.Predictivity: the area under the receiver operator characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) for classification tasks or the MSE for regression tasks.For each dataset, Stabl was compared to Lasso and EN on single-omic data or to early fusion and late fusion on multi-omic data over 50 random repetitions using a repeated five-fold cross-validation (CV) strategy. As the true set of informative features is not known for real-world datasets, the performance metrics differed from those used for the synthetic datasets:p = 37,184 cfRNA features) isolated from pregnant patients with the aim of classifying normotensive or preeclamptic (PE) pregnancies  collected from two independent cohorts  of SARS-CoV-2-positive patients to classify COVID-19 disease severity , compared to 91% for Lasso .Stabl’s reliability performance was also improved compared to Lasso and EN. The univariate p-values (Mann-Whitney test) for the features selected by Stabl were lower than for those selected by Lasso ,j or EN AUROC30% = 0.85 vs. AUROC50% = 0.49), with a model selecting no features. Conversely, for the PE dataset, fixing the frequency threshold at 30% vs. 50% resulted in a 5.3 fold improvement in sparsity with only a 6% decrease in predictivity (AUROC30% = 0.83 vs. AUROC50% = 0.78).Stabl was also compared to SS using 30%, 50%, and 80% fixed frequency thresholds (Table S2). Consistent with the synthetic modeling analyses, the predictivity and sparsity performances of SS varied greatly with the choice of threshold, while Stabl provided a solution that optimized sparsity while maintaining predictive performance. For example, using SS with a 30% compared to a 50% threshold resulted in a 42% decrease in predictivity for the COVID-19 dataset  selected by the final Stabl PE model encoded proteins with fundamental cellular function, providing biologically-plausible biomarker candidates. Other features were non-coding RNAs or pseudogenes, with yet unknown biological function (Table S3). For the COVID-19 dataset, several features identified by Stabl echoed key pathobiological mechanisms of the host inflammatory response to COVID-19. For example, CCL20 is a known element of the COVID-19 cytokine storm,26 CRTAC1 is a newly identified marker of lung function,29 PON3 is a known biomarker decreased during acute COVID-19 infection,30 and MZB1 is a protein associated with high neutralization antibody titers after COVID-19 infection .Identification of fewer and more reliable features using Stabl facilitated the biomarker discovery process, pinpointing the most informative biological features associated with the clinical outcome. For example, three out of thirteen (23%) cfRNA features . The study addresses an important clinical need for improved prediction of labor onset in term and preterm pregnancies as standard predictive methods are inaccurate.33We compared Stabl to early and late fusion Lasso, two commonly employed strategies for multi-omic modeling, on the prediction of a continuous outcome variable from a triple-omic dataset. The analysis leveraged a unique longitudinal biological dataset collected in independent training and validation cohorts of pregnant individuals, together with curated clinical information .31 The sp = 1,317 features, Somalogic), a metabolomic dataset , and a single-cell mass cytometry dataset . When compared to early and late fusion Lasso, Stabl estimated the time to labor with comparable predictivity , along with a synchronized increase in pregnancy-associated hormones , placental-derived , and immune regulatory plasma proteins .From a biological standpoint, Stabl streamlined the interpretation of our prior multivariate analyses,Application of Stabl to the three existing omic datasets demonstrated the algorithm’s performance in the context of biomarker discovery studies with a known biological signal. To complete its systematic evaluation, Stabl was applied to our newly generated multi-omic clinical study performing an unbiased biomarker discovery task. The aim of the study was to develop a model to predict which patients will develop a postoperative surgical site infection (SSI) from analysis of pre-operative blood samples . A cohorAUROCStabl = 0.80 ). When compared to early and late fusion Lasso, Stabl had comparable predictive performance  offers two main benefits. First, it expresses a trade-off between reliability and sparsity, as it is the sum of an increasing and a decreasing function of the threshold. Second, assuming exchangeability between artificial and uninformative features Stabl’s procedure guarantees a stochastic upper bound to the FDP using the reliability threshold estimate, which ensures reliability in the optimization procedure. By minimizing this function ex-ante, Stabl objectively defines a model fit from the procedure without requiring prior knowledge of the data.Stabl improves on these methods by experimentally, and, under certain assumptions, theoretically, generalizing previous false discovery rate control methods devised for model-X knockoffs and random permutation noise.+ and allows for improved reliability and sparsity compared to Lasso or EN at similar predictivity performance. When tested on real-world omic studies, Stabl also performed favorably compared to Lasso and EN. For each case study, the identification of a manageable number of reliable biomarkers facilitated the interpretation of the multivariate predictive model. Prior analyses of similar datasets31 required suboptimal analysis frameworks: either post-hoc analyses were performed using user-defined cut-offs for feature selection after an initial model fit, or features associated with the clinical endpoint were selected before modeling, thus risking overfitting. In contrast, Stabl embeds the discovery of reliable candidate biomarkers within the predictive modeling, alleviating the need for separate analyses.On a synthetic dataset, we experimentally demonstrate that Stabl selects an optimal reliability threshold by minimizing the FDP50 In contrast, the late fusion method trains the learner on each omic data layer independently and merges the predictions into a final dataset.53 In this case, although a model is adapted to each omic, the resulting model does not weigh features from different omics directly against each other. Stabl analyzes each omic data layer independently and fits specific reliability thresholds before selecting the most reliable features to be merged in a final layer, thus combining the advantages of both methods. Multi-omic data integration with Stabl was particularly useful for analysis of our newly generated dataset in patients undergoing surgery. In this case, the Stabl model comprised several features that were biologically consistent across the plasma and single-cell datasets, revealing a patient-specific immune signature predictive of SSI that appears to be programmed before surgery.Stabl extended readily to analysis of multi-omic datasets where a predictive model can utilize features from different biological systems. Here, Stabl offers an alternative that avoids the potential shortcomings of early and late fusion strategies. In the case of early fusion, all omic datasets are first concatenated before applying a statistical learner. This leads to optimization on all omics combined, regardless of the specific properties  of individual omic datasets.54 for reliability, Boruta55 for sparsity, and random forest56 or gradient boosting57 for predictivity. Additional studies are required to systematically evaluate each method’s performance in comparison to, or integrated with, the Stabl statistical framework. Finally, multi-omic data integration is an active area of research. Integrating emerging algorithms such as cooperative multiview learning58 may further improve Stabl’s performance in multi-omic modeling tasks.Our study has several limitations. Although we demonstrate the validity and performances of Stabl experimentally and theoretically under the assumption of exchangability between artificial and uninformative features, a more general theoretical underpinning of the method will require further guarantee. In addition, our evaluation of Stabl’s performance focused on fitting Lasso and EN models as gold standard SRMs. Further development of Stabl will be needed to allow for fitting of any SRM. While Stabl is designed to simultaneously optimize reliability, sparsity, and predictivity performances, other algorithms have been developed to address each of these performance tasks individually, such as double machine learningAnalysis of high-dimensional omic data has transformed the biomarker discovery process but necessitates new machine learning methods to facilitate clinical translation. Stabl addresses key requirements of an effective biomarker discovery pipeline offering a unified supervised learning framework that bridges predictive modeling of clinical endpoints with selection of reliable candidate biomarkers. Stabl enabled identification of biologically plausible biomarker candidates across multiple real-world single- and multi-omic datasets, providing a robust machine learning pipeline that we believe can be generalized to all omic data.


Retrograde catheter-induced coronary artery dissection during percutaneous coronary intervention is an exceedingly rare occurrence, and the likelihood of it extending into the aorta is even more uncommon. Typically, surgical treatment involves aortic root replacement combined with coronary artery bypass grafting. However, in this particular case, a meticulous approach was employed. By carefully guiding wires into the true lumens and placing stents in the proximal left main and left anterior descending arteries, the immediate complications were averted by obstructing the retrograde flow in the false lumen. Subsequently, an off-pump coronary artery bypass was performed using the left internal mammary artery to the left anterior descending artery, without the need to manipulate the aorta. This approach resulted in a short operation time and the absence of any other complications. Advances in cardiovascular interventional technology and stent materials are promoting an increase in the number of percutaneous coronary intervention (PCI) procedures and the number of patients undergoing complex, high risk PCI treatments. Among the catheter-induced coronary artery dissections (CICADs) that can occur during PCI, type A aortic dissection following the retrograde extension of a coronary artery dissection is one of the most life threatening and dramatic complications . In geneThe elderly woman who experienced a mid-left anterior descending (LAD) arteries dissection during PCI underwent a successful rescue and repair procedure using a combination of two approaches. The procedure involved a PCI in the left main (LM) to mid-LAD arteries, followed by an emergency off-pump coronary artery bypass graft (OPCAB) utilizing the left internal mammary artery (LIMA) to LAD.A 78-year-old woman was brought to our hospital with elevated cardiac enzymes. On admission, the electrocardiogram showed no ST elevation, but right bundle branch block; transthoracic echocardiography (TTE) showed moderate to severe hypokinesis at the anterior apex and the apical septal wall. Computed tomography (CT) showed neither aortic dissection nor pericardial effusion. With the diagnosis of non-ST elevation myocardial infarction, diagnostic coronary angiogram was performed and showed no stenosis or thrombus in LM, but diffuse, irregular, up to 85% stenosis from proximal to distal LAD and diffuse, irregular, up to 70% stenosis in the proximal to distal right coronary artery (RCA). When a mid-LAD dissection and retrograde aortic dissection appeared during PCI , the PCICICADs are rare but can be severe complications that may arise during PCI . While mIn conclusion, the most effective approach to address iatrogenic type A aortic dissection during PCI remains a topic of ongoing debate. The required treatments can encompass a combination of PCI, emergency surgery, and intensive care, which are tailored to the individual patient's condition. Our report presented a detailed case that showcased the successful integration of PCI and emergency OPCAB, aiming to enhance the quality of care and management for patients encountering iatrogenic type A aortic dissection.


Based on high-stress characteristics of prestressed anchor cables, this paper develops an axial-distributed testing method to test corrosion damage of prestressed anchor cables. The positioning accuracy and corrosion range of an axial-distributed optical fiber sensor is studied, and its mathematical model between corrosion mass loss and axial fiber strain is established. The experimental results show that the fiber strain from an axial-distributed sensor enables one to reflect the corrosion rate along a prestressed anchor. Moreover, it has a greater sensitivity when an anchored cable has a higher stress. The mathematical model between corrosion mass loss and axial fiber strain is determined to be  Prestressed anchorage technology has been widely used in engineering. However, corrosion can randomly occur along a prestressed cable, and in turn lead to its failure. Therefore, corrosion monitoring of prestressed anchor cables is of great significance to structural safety. In recent years, optical fiber sensing technology has attracted much attention in the field of reinforcement corrosion monitoring due to its incomparable advantages such as small size, soft winding, ease of compositing with structure, corrosion resistance, and distributed measurement.The measurement methods of environmental parameters such as step-type corrosion fiber sensor ,2,3, lonOn the other hand, the corrosion measurement of reinforcement also depends on the expansion of reinforcement corrosion products using fiber grating sensors and fully distributed optical fiber sensors. For example, some scholars have proposed a Fe-C film FBG sensor ,8,9, cirSimultaneously, the fully distributed optical fiber sensing system uses an optical fiber as both the sensing element and signal transmission medium to detect the changes of temperature and strain along different positions of optical fiber in order to realize a truly distributed measurement. Zhao Xuefeng designed a white light interference corrosion sensor , a BrillTherefore, to decrease light loss and improve the signal-to-noise ratio, this paper proposes an axial-distributed measurement method. The mathematical model of corrosion mass loss rate and axial-distributed optical fiber strain is ultimately established, as well as a long-term monitoring method of corrosion damage of prestressed anchor cable, which enable one to solve the "bottleneck" of corrosion monitoring methods.Prestressed anchor cables are characterized by a high-tension stress. The corrosion of prestressed anchor cable eventually leads to a reduction in cross-sectional area. Correspondingly, the strain along anchor cables with a high prestress increases at the corrosion site. Equation (1) shows the strain is dependent on its cross-sectional area.Therefore, the distributed strain along the anchor cable indicates its corrosion. Furthermore, the strain difference caused by cable corrosion under high-stress state is deduced:Corrosion of prestressed anchor cable causes the section loss of strand steel, and the section loss rate is equal to its theoretical mass loss rate Then, Equation (3) is derived:Subsequently, the cable strain is expressed as2. Substituting the above data into Equation (5), the relation between cable strain and corrosion loss rate is obtained. Cable strain is attained by an axial-distributed sensor. Hence, it measures the corrosion mass loss rate using the distributed strain along the anchor cable.In the practical engineering, the tensile force of a monofilament strand with a 5 mm diameter is approximately 24 kN, its elastic modulus approximately 200 GPa, and its initial cross-sectional area is 19.625 mmTo establish a mathematical model of the axial strain and the corrosion mass loss rate of prestressed anchor cable, a tensile test was conducted for different specimens with a variety of sectional losses. The specimens with different sectional losses correspond to different corrosion mass losses. The strand dimensions used were 5 mm in diameter and 1 m in length. The tensile strength was 1860 MPa and the elastic modulus was approximately 200 GPa. The graded loading method was used in this experiment. There were 1#, 2#, 3#, 4#, 5#, 6#, 7# specimens with a reduced sectional area fabricated to simulate different corrosion loss rates and corrosion lengths . The secIn order to ensure full contact between the optical fiber and the strand, and to avoid errors caused by strain transfer, the strand was first polished with sandpaper to make its surface smooth. In order to prevent the impact of grinding scraps, stains, etc., alcohol was used clean the impurities. A 0.9 mm tight optical sensing fiber was glued with AB epoxy glue along the steel strand afterwards. These optical fiber sensors have a higher strain transfer. The specimens are shown in −5 GHz/μɛ. Both ends of the axial-distributed sensor were connected to the Pump and Probe end of NBX-6040 BOTDA. The minimum sampling interval and spatial resolution was 5 cm and 10 cm, respectively. The experimental setup is detailed in An axial-distributed optical fiber sensor was used to simultaneously monitor the stress and strain of the prestressed steel strand specimens. The PPP-BOTDA measures spatially distributed strains along the optical fiber through measuring the time of flight of light from Brillouin backscattering ,23,24. Tnge (Δε) :(6)ΔvB=Cussed in . The strThere is a simultaneous strain peak along the strand in 2 due to manufacturing error. The strand section itself is a random variable due to fabrication error, resulting in a calculable section loss rates error. Moreover, the error depends on the increasing tension. Therefore, the corrosion mass loss rate should not be attained by Equation (5). The parameters of At the same time, based on the above conclusions of corrosion mass loss rate from Equation (4), we used Equation (4) to attain the measured corrosion mass losses of these specimens under different tensions. A ratio in Equation (4) was calculated between two average strain values whereas, in Equation (5), only one strain value was used. Since this value was inaccurate, the corrosion mass loss rate had an error owing to Equation (5). The experimental results are shown in To summarize, the higher tension and Equation (4) improve the precision of the measured corrosion mass loss rate. The strain data are collected from the non-corroded part of the axial-distributed sensing fiber. The actual calculated corrosion rate is basically consistent with the measured corrosion rate from the collected fiber strain, as shown in Finally, the mathematical model between the corrosion rate and the measured strain from an axial-distributed sensor is acquired. (1)A long-term monitoring method of corrosion damage of prestressed anchor cable is proposed. The results show that the corrosion of the prestressed anchor cable can be monitored by the axial-distributed sensor. Moreover, it has a greater sensitivity when an anchored cable has a higher stress. Its mathematical model relating corrosion mass loss and axial fiber strain is (2)The corrosion length of anchor cable is characterized by the axial distribution fiber strain. The position accuracy depends on its corrosion sensitivity. The axial-distributed optical fiber sensor is used to accurately locate the corrosion damage of the prestressed anchor cable;(3)The theoretical and experimental curves in This study develops a new method to investigate the corrosion mass loss rate mathematical model with the use of an axial-distributed optical fiber optic sensor. Based on this study, the following key findings can be drawn:


Sponge-grade Archaeocyatha were early Cambrian biomineralizing metazoans that constructed reefs globally. Despite decades of research, many facets of archaeocyath palaeobiology remain unclear, making it difficult to reconstruct the palaeoecology of Cambrian reef ecosystems. Of specific interest is how these organisms fed; previous experimental studies have suggested that archaeocyaths functioned as passive suspension feeders relying on ambient currents to transport nutrient-rich water into their central cavities. Here, we test this hypothesis using computational fluid dynamics (CFD) simulations of digital models of select archaeocyath species. Our results demonstrate that, given a range of plausible current velocities, there was very little fluid circulation through the skeleton, suggesting obligate passive suspension feeding was unlikely. Comparing our simulation data with exhalent velocities collected from extant sponges, we infer an active suspension feeding lifestyle for archaeocyaths. The combination of active suspension feeding and biomineralization in Archaeocyatha may have facilitated the creation of modern metazoan reef ecosystems. Following the dominance of microbial reefs in the Tonian and Cryogenian, the first metazoan reefs emerged during the Ediacaran–Cambrian transition . In partArchaeocyaths were globally distributed during the early Cambrian approx. 530–509 Ma), forming metre-scale bioherms akin to modern patch reefs 0–509 Ma, to much Given their porous morphology, archaeocyaths have been interpreted as macroscopic suspension feeders ,13,23,24. 2. 2.1Favilynthus mellifer and chalice-shaped Archaeolynthus porosus) based on their availability of detailed systematic descriptions, body and pore morphological descriptions, and published fossil photographs [figure 1a–e; electronic supplementary material, table S1). We also replicated the double-walled idealized aluminium models used by Balsam & Vogel [figure 1e,f).We used Autodesk Fusion 360 to construct digital models of three archaeocyaths . All modtographs ,30  = 997 kg m–3, dynamic viscosity (μ) = 0.001 kg s–1 m–1) were assigned to the domain surrounding the model.Three-dimensional, incompressible fluid flow simulations were performed using COMSOL Multiphysics. CFD is optimal for investigating small-scale flow patterns both within and surrounding the cavity and pores, and allows experimentation with multiple morphotypes and simulated flow velocities. We used hexahedral flow domains  with the organism placed approximately one third of the length along the lower surface. The fluid properties of water . Because of the geometric and computational complexities of the A. porosus CFD simulations, the domain and model were bisected down the model's centre, permitting its symmetry to be used necessitating only half the domain to be solved.A stationary Reynolds averaged Naiver Stokes (RANS) shear stress transport (SST) turbulence model was used with a slip condition applied to the upper surface of the flow domain and sides parallel to flow. No-slip conditions were prescribed along the seafloor and organism model where velocity approaches 0 cm s−1  were slower in the cavity of the models than in the far field, and vortex shedding occurred off the upper surfaces of the models. Recirculation patterns within the wakes were evident downstream of the models, and the largest velocity magnitudes occurred within the upstream pores of the models. These patterns strengthened and weakened with increasing and decreasing inlet velocities, respectively. The lower velocity simulations (inlets < 15 cm s−1) developed viscous sublayers that were greater in vertical height (F. mellifer); and chalice-shaped taxon (A. porosus). Lastly, exhalent velocity ranges for each species model are shown in Some flow patterns were consistent across all simulations . These cavity flow patterns were consistent for all far field velocities, though absolute cavity velocities scaled with far field velocity.The cylindroconical . 3.3A. porosus was typically isolated to the upper third of the cavity with the remainder approximately stagnant. A larger zone of circulation formed within the upper one-third of the central cavity, with water then redirected towards the model base along the downstream face, until it was forced upwards towards the upstream face, finally exiting through the osculum. The downward flow of the circulation zones at the base of the model had the greatest absolute velocity within the skeleton  velocity simulations showed stagnation within the lower two-thirds of the central cavity.Fluid flow for the chalice-shaped . 4At one time, debate surrounded the ability of sponges to take advantage of passive flow. Vogel  proposedOur BVM CFD results broadly replicate Balsam & Vogel's experimental results , which d3 s−1 in shallow reef environments with typical ambient flows of 0–36 cm s−1 . Across the various inlet velocities, the ent flow .3 s−1 and 0.8 cm3 s−1, respectively, while never surpassing the lowest end of typical exhalent velocities reported for sponges  for most inlet velocities, suggesting that most of the water within the internal cavity was not effectively exiting the osculum, therefore preventing unfiltered water from passing through the porous walls/intervallum. At the slower far field velocities typical of lagoonal communities , flow wThe combination of fossil scaled CFD results and exhalent rates of modern sponges suggest that if archaeocyaths were obligate passive suspension feeders, then they would require much slower metabolism or far greater concentrations of nutrients in the water column, the latter of which is unlikely given the prevalence of photoautotrophic reef dwellers in close association  and a reWe note that our models only replicate the archaeocyath skeletal tissues; in life, archaeocyaths would also have incorporated soft tissues enveloping the skeleton and probably within the walls and cavity. However, as our BVM two-walled comparisons have demonstrated, it is highly unlikely that this additional morphological complexity would have allowed for more efficient flow. We hypothesize that the soft tissue volumes filling additional space (and associated increase in texture) would probably have further slowed fluid flow through their skeleton and decreased accessible pore diameter even more, though further testing of this hypothesis is needed. Finally, owing to computational constraints on the modelling, most of our models represented single-walled archaeocyaths rather than the more common two-walled (cone-in-cone) morphology, which would have also further slowed the transfer of water from the external wall to the inner chamber, further reducing fluid circulation.. 4.1Reconstructing archaeocyaths as active suspension feeders has significant palaeobiological implications for understanding Cambrian reefs. Most extant sponges rely on actively pumping water through their skeletons, which has allowed them to expand to almost all marine environments and adapt to varying nutrient loads and sources ,45. An aIn comparison to modern reefs, archaeocyath reefs would have had a much greater proportion of active suspension feeders; although sponges, bivalves, and other active filterers are important components of Phanerozoic coral reefs . With th


Ulcerative colitis (UC) develops as a result of complex interactions between various cell types in the mucosal microenvironment. In this study, we aim to elucidate the pathogenesis of ulcerative colitis at the single-cell level and unveil its clinical significance. Using single-cell RNA sequencing and high-dimensional weighted gene co-expression network analysis, we identify a subpopulation of plasma cells (PCs) with significantly increased infiltration in UC colonic mucosa, characterized by pronounced oxidative stress. Combining 10 machine learning approaches, we find that the PC oxidative stress genes accurately distinguish diseased mucosa from normal mucosa . Using MCPcounter and non-negative matrix factorization, we identify the association between PC oxidative stress genes and immune cell infiltration as well as patient heterogeneity. Spatial transcriptome data is used to verify the infiltration of oxidatively stressed PCs in colitis. Finally, we develop a gene-immune convolutional neural network deep learning model to diagnose UC mucosa in different cohorts . Our work sheds light on the key pathogenic cell subpopulations in UC and is essential for the development of future clinical disease diagnostic tools. Ulcerative colitis (UC) is a chronic, idiopathic, inflammatory disease that affects the colonic mucosa ,2 . TheMechanistically, the development of UC can be resulted from synergistic interactions of multiple epithelial, immune, and stromal cells. Dysfunctions of these cells might contribute to the diseasevia machine learning and are associated with multiple immune cell infiltrations. We also verified the existence of oxidatively stressed PCs in colitis through spatial transcriptomics. Finally, we synthesized photographs of 11 key genes and immune cells to establish a gene-immune convolutional neural network deep learning model for the diagnosis of UC.In this work, we found a significant increase in plasma cell subpopulations in UC patients by single-cell sequencing analysis. We identified specific PC gene modules by high-dimensional weighted gene co-expression network analysis (hdWGCNA). Protein‒protein interaction (PPI) network analysis and enrichment analysis showed that the upregulated plasma cell subpopulation is mainly associated with oxidative stress. PC oxidative stress genes facilitate UC diagnosishttp://www.ncbi.nlm.nih.gov/geo/), including single-cell sequencing data GSE182270All UC mucosa datasets were screened in the Gene Expression Omnibus (GEO) database  was used to further identify potential interaction networks between target module proteins [The GeneMANIA data resource . A deep learning model based on the Keras and TensorFlow frameworks using convolutional neural networks (CNNs) was constructed.Univariate logistic regression was utilized to identify key diagnostic genes in plasma cells characterized by oxidative stress. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) was employed to further filter the variablesP value<0.05 was considered statistically significant. All analyses were performed in R (version 4.1.3).The Wilcoxon test was used to make comparisons between the two groups. The Kruskal‒Wallis test was used to make comparisons among the three groups. Pearson correlation analysis was used to reveal the relationship between the 11 key genes and 10 types of immune cells. ATo explore the altered cellular composition in UC mucosa compared to normal mucosa, we applied GSE182270 to dissect the cellular composition of UC. After integration by harmony, dimensionality reduction and clustering and cell type annotation, we discovered that the altered proportion of plasma cells (PCs) was most pronounced in UC A. To furSupplementary Table S1). Furthermore, pseudotime analysis indicated that Cluster 9 is at the end of plasma cell development  analysis for the blue module genes. We noticed that the genes at the center of interactions were mainly involved in oxidative phosphorylation, ATP synthesis and metabolic pathways A. MoreovSupplementary Table S2). In integrated machine learning, we incorporated 10 machine learning algorithms, including LDA, SVM, XGBoost,etc. In the training set GSE87466, we first conducted 10 repetitions of 5-fold cross-validation to examine the stability of each model. It was found that most of the models possessed good stability. Among the algorithms, RPART and XGBoost performed poorly, while SVM exhibited the best performance . Remarkably, there was colocalization of plasma cell abundance and abundance of cellular stress in space, as indicated by the white arrow  heatmap for each patient, with the value of each square being the ratio of the expression of a gene to the infiltration of a particular immune cell in that patient A. The coUC is a chronic gastrointestinal disorder of unknown origin characterized by continuous inflammatory colonic mucosa. Understanding the cellular and molecular mechanisms underlying UC has been a challenge, but recent advances in single-cell RNA sequencing and machine learning have enabled investigation of oxidative stress-related gene-gene and cell-cell interactions in UC. In this study, we used single-cell RNA sequencing and high-dimensional weighted gene co-expression network analysis to identify a disease-specific subgroup of plasma cells characterized by oxidative stress. Additionally, we identified signature genes of oxidatively stressed plasma cells that could be used for disease diagnosis and immune microenvironment analysis.Plasma cells play a critical role in maintaining intestinal homeostasis, but their involvement in UC remains understudied. A highly dysregulated B-cell response in UC has been previously demonstrated, mainly reflected in the expansion and decreased diversity and maturation of plasma cellsOxidative stress has been recognized as a potential risk factor for UCB cells are particularly vulnerable to oxidative stress, which can lead to B-cell dysfunction and contribute to the pathogenesis and progression of various diseases, including nonalcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma associated with nonalcoholic steatohepatitis (NASH)SLC25A3 encodes the mitochondrial phosphate carrier, whose mutation can lead to mitochondrial phosphate-carrier deficiencySLC25A3 is also differentially upregulated in UC and plays a potential carcinogenic role in UC-associated colorectal cancerXBP1 abnormalities result in intestinal inflammation, thus increasing susceptibility to IBDCOX6A1 is involved in oxidative phosphorylation and mitochondrial respiration [PPIB helps to adapt cells to oxidative stress and hypoxic conditionsARF4 has been demonstrated to have anti-apoptotic activity in glioblastoma by inhibiting stress-mediated apoptotic signalsPRDX4 has been reported to ameliorate lipotoxicity-induced oxidative stress and apoptosis in diabetic cardiomyopathyNDUFAB1 encodes one of the mitochondrial respiratory chain complexes, which plays an essential role in mitochondrial homeostasis in colitisMYL6 is strongly related to increased mitochondrial efficiency, especially under oxidative stress conditions. In summary, almost all the genes in the blue module are strongly related to oxidative stress. Meanwhile, UC mucosa scored significantly higher by the ssGSEA algorithm based on genes in the blue module. Additionally, the expression pattern of blue module genes is closely associated with immune infiltration, indicating their possible role in immunomodulation. To date, endoscopy is currently the gold standard for evaluating UC lesions, but misdiagnosis of UC can occur due to its complex and varied clinical manifestations. Molecular diagnosis based on RNA sequencing of colon tissue has significantly improved diagnostic accuracyThe diagnosis of UC is mainly based on clinical presentation and endoscopic findingsiration ,60 , buIn conclusion, we successfully identified a disease-specific subgroup of plasma cells characterized by oxidative stress in UC. Our study has established promising machine learning and deep learning models based on PC oxidative stress genes, providing a new paradigm for future UC research.Supplementary


Dear Editor,1 The vagina is a tubular tract made of fibromuscular and elastic tissue that connects the cervix to the outer genitals. Its main function is to discharge uterine secretions.2 The vaginal epithelium (VE) is a keratinized, stratified squamous epithelium consisting of three layers: the basal layer, the suprabasal layer, and the apical cornified layer.3 Estrogens induce the proliferation of basal epithelial cells in the vagina. The suprabasal cells, which are no longer mitogenic, differentiate as they move up through the epithelium. The apical cells undergo keratinization, lose their nuclei and cytoplasm, and eventually shed from the surface.4 With multiple sexually transmitted diseases posing a significant threat to human health,5 it is increasingly important to understand how the vaginal epithelium regenerates to maintain homeostasis and how it differs from the neighboring cervical epithelium.The integrity of the vaginal epithelium is crucial for women's reproductive health and for providing protection against HIV and sexually transmitted infections.In this study, we extracted vaginal tissue from five adult virgin mice and conducted single-cell RNA sequencing (scRNA-seq) on the vaginal epithelium. We obtained a total of 7823 cells and isolated and sequenced 6187 cells from the combined duplicates that passed QC filtering. Using Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) projection, we clustered the cells into six distinct clusters Fig. . To idenOur scRNA-seq data revealed the existence of two distinct types of vaginal suprabasal cells. To investigate their correlations and the continuity of differentiation in the vagina, we utilized the reversed graph embedding method to computationally infer the differentiation trajectory of vagina epithelial cells from the scRNA-seq atlas through pseudo-time mapping Fig. . Our anaEsr1, Fzd10, and Tcf7) were highly expressed in basal cells but started to decrease in Fabp5-high suprabasal cells. In contrast, Notch signaling genes  showed low expression levels in basal and Fabp5-high suprabasal cells but began to increase in Ifitm3-high suprabasal cells and terminally differentiated cells  to examine the biological implications of genes that were significantly up-regulated in these two cell types. Our findings indicated that both types of columnar cells were enriched in genes related to secretion, such as those involved in vesicle and multivesicular body organization, which are essential for cell secretion and transport. Additionally, as the first line of defense in the lower reproductive tract, vaginal columnar cells are primarily responsible for secreting anti-microbial substances, which are accompanied by a stronger leukocyte chemotaxis response. Vaginal columnar cells were also found to have higher levels of apoptotic signals and more prominent cell renewal compared to cervical columnar cells. Furthermore, both organs were enriched in estrogen-related signals, although the cervix exhibited slightly higher enrichment levels  and pathway analyses (els Fig. . These rin vivo. They may also aid in developing novel cell-based therapies in the future.In our study, we classified vaginal epithelial cells into six distinct clusters, including two subpopulations of suprabasal epithelial cells with a defined temporal differentiation sequence. Additionally, we uncovered several unexpected dissimilarities between cervical and vaginal epithelial cells. Our findings carry significant implications for comprehending the role of vaginal stem/progenitor cells in maintaining tissue homeostasis, regeneration, and disease progression pbad006_Supplemental_FileClick here for additional data file.