The human genome produces various types of RNA molecules. A significant portion of these are long non-coding RNAs (lncRNAs) exceeding 200 nts without an obvious open reading frame. There are more than 200,000 lncRNAs, but we have limited understanding of their functions. LncRNAs can influence gene activity by interacting with proteins or nucleic acids. Some lncRNAs regulate genes involved in cancer, either promoting or suppressing tumor growth. One lncRNA of interest is ANRIL (Antisense Noncoding RNA in the INK4 Locus), which can affect gene expression through different ways. However, ANRIL’s exact role in cancer is complex and varies in different situations, making it a challenging area to study. This review strives to offer a thorough comprehension of ANRIL’s role in regulating genes and its impact on the development of cancer.9p21 region, is a critical factor for regulating gene expression by interacting with multiple proteins and miRNAs. It has been found to play important roles in various cellular processes, including cell cycle control and proliferation. Dysregulation of ANRIL has been associated with several diseases like cancers and cardiovascular diseases, for instance. Understanding the oncogenic role of ANRIL and its potential as a diagnostic and prognostic biomarker in cancer is crucial. This review provides insights into the regulatory mechanisms and oncogenic significance of the 9p21 locus and ANRIL in cancer.ANRIL (Antisense Noncoding RNA in the INK4 Locus), a long non-coding RNA encoded in the human chromosome  A significant part of the resulting transcriptome corresponds to the long non-coding RNAs (lncRNAs) defined as RNAs longer than 200 nts and lacking obvious open reading frames. The number of lncRNAs exceeds 200,000 and the field lacks evidence supporting the functionality of most of them ,2,3,4. Cctively) ,7. The dctively) .Cellular experiments have shown that lncRNAs are involved in a wide spectrum of biological processes ranging from cell proliferation, apoptosis and nutrient sensing to cell differentiation ,10. ConsCancer is one of the leading causes of death worldwide and refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells capable or not of invading nearby tissues and organs. Aberrant expressions and splicing profiles of lncRNAs have been found in various types of cancer in agreement with the fact that some lncRNAs act as oncogenes, promoting cancer development and progression, while others act as tumor suppressors, inhibiting cancer growth and metastasis ,15. For 9p21 locus in several linear and circular spliced variants mainly made of repetitive elements derived from LINE, SINE and LTR sequences [In this review, we focused on the implication of the lncRNA ANRIL  in cancers. ANRIL is transcribed from the equences ,19,20. Tequences .ANRIL can modulate gene expression at the post-transcriptional level by acting as a competing element RNA (ceRNA) of miRNAs and proteins ,24. AddiThe proposed roles of ANRIL in cancer biology are still unclear. This is mainly due to differences in the types of cancer studied and/or the methods used to evaluate ANRIL expression likely to exclude the functional contribution of the individual ANRIL isoforms for instance. It is also possible that ANRIL has different activities in different stages of cancer progression or in different cell types. This drastically complicates the understanding of the mechanisms through which ANRIL promotes cancer development. In this review, we addressed these points and made efforts to formalize a clearer vision of the intertwined domain related to ANRIL and cancers.9p21 locus is located on the short arm (p) of chromosome 9 at region 21 and spans over 170 kilobases (kb). This locus has drawn particular attention due to the frequent observation of homozygous deletions or epigenetic modifications, such as DNA methylation-induced transcriptional silencing resulting in the inactivation of this genomic region in multiple cancer types. Additionally, single-nucleotide polymorphisms (SNPs) within this region are linked to several age-related disorders, CVD and cancers. These SNPs are predominantly found within the genes themselves and the adjacent gene desert region spanning approximately 0.3 megabases [The egabases . Note th9p21 locus was initially described as containing the CDKN2A/ARF and CDKN2B genes which encode three proteins involved in cell cycle regulation: the p14/ARF, p16INK4a/CDKN2A and p15INK4b/CDKN2B proteins, respectively [CDKN2A/ARF gene was found to be frequently inactivated or mutated in melanoma and other cancers, at least in part due to its ability to modulate the p53 axis [9p21 locus to various other abnormal situations, including atherosclerosis, T2D, Alzheimer’s disease, lupus erythematosus, epilepsy, glaucoma, obsessive compulsive disorder and sepsis [The ectively . Initialp53 axis . Furtherd sepsis ,36,37,389p21 locus. It was promptly recognized as a critical factor in controlling the expression of this locus [Pasmant and colleagues made a significant finding in 2007 by identifying the long non-coding RNA ANRIL encoded within the is locus . As previs locus ,39.9p21 locus includes the gene that encodes the long non-coding RNA ANRIL. This gene is orientated in the opposite direction (antisense) to the genes within the locus and extends over a region of 126 kb, covering the CDKN2B gene located within its first intron. The intergenic region separating the ANRIL gene from the CDKN2A/ARF gene is a bidirectional promoter long to approximately 300 bp [As mentioned above, the y 300 bp ,40,41.CDKN2B, it is compelling to consider ANRIL a critical contributor to many of the pathological processes dependent on the 9p21 locus.The CDKN2A and CDKN2B proteins are cyclin inhibitors that regulate cell cycle progression in the G1/S phase by inhibiting the association between CDK4/6 and cyclin D . In the As the vast majority of the lncRNAs, ANRIL expression is modest (less than 1000 copies per HEK293 cells) and tissue-specific ,45. ANRI9p21 locus is contained within a single topologically associated domain (TAD) [CDKN2A/ARF, CDKN2B, ANRIL genes in HUVEC cells and in the colon-cancer-related cell lines GES1, BGC823 and H1299 [9p21 locus [Since ANRIL is crucial for maintaining cellular homeostasis, the regulation of its expression is a very sensitive issue ,49. Firsin (TAD) ,51. Thisnd H1299 ,52,53. A21 locus .ANRIL expression is regulated by multiple factors and stimuli-dependent mechanisms such as genotoxic stress and inflammatory response ,41,52,55CDKN2A/ARF-ANRIL genes contains DNase I hypersensitivity regions and CpG islands. Interestingly, the methylation of these elements has been shown to inhibit the association of CTCF, resulting in a decrease in ANRIL expression associated with H3K4me3 reduction [As mentioned before, the bidirectional promoter of the eduction . Note theduction .Conversely, the interaction between ERα (Estrogen Receptor Alpha) and its putative binding site located in this promoter region is facilitated by CpG methylation, resulting in increased ANRIL expression upon β-estradiol treatment of SW872 cells . The metβ1, phosphorylated (p)-SMAD2/3, SMAD4, and N-cadherin reduction [ANRIL expression is further modulated by the oncogenic transcription factors c-MYC, SOX2, and SP1 in lung, pharynx, and liver cancers, respectively ,63,64. Meduction .Additionally, post-transcriptional mechanisms govern ANRIL abundance. In the context of colon cancer, it has been proposed that the association between AUF1 and ANRIL may lead to a detrimental effect on ANRIL stability and that the presence of P14AS lncRNA reduces the interaction between AUF1 and ANRIL. This competitive binding of P14AS lncRNA to AUF1 leads to an increase in ANRIL expression level . In the LncRNAs exhibit lower levels of conservation compared to protein coding genes, and ANRIL is no exception to this trend. Through a phylogenetic analysis involving 27 species (from zebrafish to human), it was determined that ANRIL originated in placental mammals with a limited number of exons. As a result of specific evolutionary processes, an increased number of exons became evident within the haplorhines cladus, while exons’ erosion was observed during rodent evolution, limiting the use of murine models to study the association between ANRIL and cancers. The primate-specific expansion was attributed to the insertion of transposable elements (TE) within existing exons or introns, resulting in the modification of its functional capabilities . Indeed,ANRIL gene consists of 21 exons, with lengths ranging from 74 to 696 nucleotides (mean length of 202 nucleotides) (The eotides) . These eEZR and CXCL11 genes, as well as the up-regulation of TMEM106B. Conversely, overexpression of the isoform 13 has the opposite effect on these genes. These antagonistic activities are also evident at the cellular level of physiological processes. The expression of the isoform 9 appears to promote transmigration and cellular adhesion, while the isoform 13 elicits contrasting effects [ effects .In addition, at least 30 circular isoforms have been identified, which include exons 4 to 16. Circular RNAs (circRNAs) are a type of lncRNA that are abundant, evolutionarily conserved, and often exhibit tissue-specific expression patterns. They are generated through a process called back splicing, and possess unique properties such as resistance to exonuclease degradation due to their lack of 5′ and 3′ ends . CircRNAANRIL has undergone extensive analysis, with researchers employing various methodologies such as RTqPCR, RNA sequencing, or microarrays across numerous cancer cell lines and tissues. In an effort to gain clarity, a review of the literature has been performed and 76 articles have been sorted based on several criteria, such as the accessibility of primer sequences employed in RTqPCR, to compile a comprehensive inventory of the ANRIL isoforms identified in each investigation. The results are indicated in As mentioned before, it is possible that ANRIL has different activities in different stages of cancer progression. This is consistent with the fact that ANRIL expression is significantly correlated with a higher TNM stage of cancers, including LC, GC, HCC, LSCC, OS, CRC, CC, OC, HCC, BladC and OS . In addiAltogether, these data strongly suggest that the majority of the linear ANRIL isoforms likely possess tumor-promoting capabilities. Since linear isoforms are mainly nuclear, one reasonably hypothesizes that ANRIL over-expression generates excess ANRIL molecules capable of reinforcing the regulatory activity it exerts on its direct targets or even on additional genomic regions. The molecular aberrations thus generated, including, for instance, inappropriate gene modulation, could be responsible for the appearance and/or reinforcement of pathological processes. In agreement, cell line experiments have shown that a reduction in ANRIL leads to increased apoptosis and senescence while decreasing cell proliferation, invasion, and migration.To conclude, note that the relative levels of circular isoforms of ANRIL have not been systematically explored in most studies. Only two articles have reported an upregulation of circular ANRIL isoforms in cancer, specifically in CC and melanoma ,91. AlthMTAP-ANRIL fusion is also critical when assessing the involvement of ANRIL in cancer biology. This fusion gene, documented to exhibit a frequency exceeding 7% in melanoma, results from a chromosomal rearrangement between ANRIL and MTAP, a tumor suppressor gene involved in purine metabolism (methylthioadenosine phosphorylase) [MTAP-ANRIL gene fusion leads to the suppression of the wild-type MTAP expression and facilitates an epithelial–mesenchymal transition-like process through the activation of JNK and p38 MAPK pathways, both in vitro and in vivo [The contribution of the orylase) . In this in vivo .9p21 locus have been the topic of several genome-wide association studies (GWAS), unveiling ANRIL sequences as a hotspot for single nucleotides polymorphisms (SNPs) that are susceptibility factors for several pathologies such as CVD, T2D and cancers [Since its first association with pathologies in 2007 , the 9p2 cancers ,164. Foc cancers , with la cancers , with hi cancers , and lin cancers   as it is described for CVD [CDKN2B-CDKN2A gene cluster, thereby playing a causal role in predisposing cancer risk [9p21 locus also depends on the methylation state of the CpG island which modulates the association of CTCF to the ANRIL promoter [ZPBP2 gene in the context of asthma [A total of 76% of the SNPs are localized in introns, 17% in downstream, and 6% in upstream sequences. Note that among all these SNPs, the expression of ANRIL has not been systematically evaluated, which makes the correlation between risky genotype and ANRIL expression difficult. However, it is possible to consider two levels that are likely to be affected by these variations. Firstly, at the DNA level, these variations may have an impact on the expression of the  for CVD ,217,218.cer risk . As mentf asthma .At the RNA level, sequence variations have the potential to impact protein binding sites and structural elements. This concept has been investigated in the context of a risky genotype associated with myocardial infarction, specifically with regard to rs10965215 and rs10738605. It is hypothesized that these variations reduce the free energy of ANRIL secondary structures which may interfere with the binding of proteins and consequently with the ANRIL activities . SNPs maThe association between cancer biology and the cytoplasmic isoforms of ANRIL primarily stems from their capacities to function as competing endogenous RNAs (ceRNA). This functionality arises from ANRIL’s ability to form hybrid complexes with miRNAs when it is overexpressed in cancer cells. Consequently, this interaction results in the decreased activity of tumor-suppressor microRNAs, ultimately leading to the upregulation of genes that are normally repressed by these miRNAs . To dateInterestingly, the ceRNA activity of ANRIL is associated with chemoresistance. It has been shown that ANRIL acts as ceRNA for the miR-125a in the development of TNBC and its chemoresistance of doxorubicin by enhancing glycolytic activity, specifically targeting enolase 1 (ENO1) . ANRIL iRadiotherapy is an important treatment for cancer, mainly by triggering DNA double-strand breaks to induce cell death. Promoting DNA damage repair can decrease the radiosensitivity of tumor cells. Since ANRIL has been described to be a key regulator in DNA damage repair (HDR), it is well suited to play a role in this process. In agreement, ANRIL positively acts on HDR by modulating the miR-7-5p/PARP1/RAD51 and miR-145-5p/MMP1 pathways in LC and TNBC respectively ,228.Although the majority of studies do not distinguish between circular and linear isoforms of ANRIL, and only a limited number of them assess the functional interactions between ANRIL and miRNAs through techniques such as dual-luciferase assays, these findings emphasize the complex regulatory interplay between ANRIL, miRNAs and cancer.cis-regulator by facilitating the recruitment of the Polycomb group proteins (PcG) to the 9p21 locus. This results in the transcriptional repression of, at least, the CDKN2B gene through the deposition of H2AK119ub (PRC1) and H3K27me3 (PRC2) histone marks at the promoter region of the CDKN2A/ARF-ANRIL genes in PC and normal lung fibroblast  located in ANRIL Exon1 and the CDKN2B promoter [9p21 locus [Another point which also remains to be elucidated is how ANRIL associates the genome to exert its functions at specific loci. It has been proposed that the -promoter . Triplex21 locus .9p21 locus . Consistent with this notion, ANRIL is capable of epigenetically suppressing the transcription of the p21 and KLF2 (Kruppel-like factor 2) genes in HCC and LC. This repression occurs through ANRIL binding to the PRC2 complex and subsequently to its recruitment to the promoters of these genes [Several studies have provided evidence suggesting that ANRIL exerts direct regulation on gene expression beyond the 21 locus . Indeed,21 locus ,254. The21 locus . In a mopoptosis . To distpoptosis . Similarse genes ,256. It se genes . This fiIL6 and 8 (interleukins 6 and 8) genes in HUVEC cells in response to a TNF-α pro-inflammatory signal [NUF2 at the chromatin level. This activation occurs through the deposition of local modifications of H3K27ac and H3K4me3 histone marks [NOX1 gene, resulting in its transcriptional activation.As previously mentioned, ANRIL has the potential to activate gene expression. According to Zhou et al., they elucidated the mechanism through which the ANRIL/YY1 complex contributes to the activation of y signal . Furthery signal ,258. Regne marks . Moreovene marks . This co-trans activity in epigenetically down-regulating the expression of these miRNAs. Such mechanisms have been already reported in various contexts, including HCC, where ANRIL enhances cancer migration and proliferation by modulating the expression of the miR-191 and by subsequently affecting the vimentin expression [-trans activity is not fully understood yet. However, the ChIRP-seq data generated by Alfeghaly and colleagues allowed for the identification of 3227 binding sites across the genome in HEK293 cells. Interestingly, among the sequences of these binding sites, 98% are enriched with G/A repeats. Moreover, ANRIL exon 8, which is 70% covered by the subcategory of LTR named ERVL-MaLR, is involved in its genomic occupancy in HEK293 cells by forming triple helices for instance [Note that approximately 24% of the miRNAs identified as ANRIL targets in the context of its ceRNA activity do not exhibit apparent hybridization sites on ANRIL  ,243,245.pression . Additiopression . The mecinstance ,262. Anoinstance ,264. FurGenomic occupancy assays performed in HEK293 cells revealed that the majority of the binding sites for ANRIL are located in non-coding regions, including introns and intergenic regions. Remarkably, 40.3% of the ANRIL sites are intronic, and, more importantly, 24% of the genes contacted by ANRIL in HEK293 are affected in terms of alternative splicing (AS) upon ANRIL knockdown . In addi-trans acting regulatory factors [trans-acting regulatory factors, or even directly recruiting components of the spliceosome [Cancer-specific splicing events have been detected in diverse cancer types such as BC, LC, PC, and AML/ALL ,267,268. factors . In addi factors . For insiceosome ,276,277.CDKN2B. Additionally, ANRIL acts as a ceRNA, impacting the activity of over 20 miRNAs involved in various cellular processes such as cell proliferation, invasion, inflammation, and EMT. Consequently, the dysregulation of ANRIL disrupts normal cell cycle control, leading to uncontrolled cell growth and the development of cancer. This explains why ANRIL is found to be upregulated in more than 20 types of cancer, as well as in conditions like CVD and T2D. To the best of our knowledge, ANRIL is the lncRNA most strongly associated with these pathological conditions.In conclusion, ANRIL is a critical factor in maintaining the balance of cellular functions despite its relatively simple nature. It plays a crucial role in regulating the progression of the cell cycle by interacting with PcG and guiding them to specific genomic loci, resulting in the silencing of genes like While substantial progress has been made in unraveling the functional roles of ANRIL in cancer, there is still much to discover. The precise molecular mechanisms underlying ANRIL activities, particularly its PcG-independent functions and its role on the splicing modulation, remain incompletely understood. In addition, the complexity of ANRIL isoforms allows for versatility in its interactions with different proteins, RNA molecules, and genomic regions, thereby influencing various biological processes. The investigation of the contribution at the molecular level of each isoform individually represents a major challenge in the field. Thus, ANRIL stands as a fascinating and multifaceted molecule with diverse implications in cancer biology. Its dysregulation makes it a promising area for continued investigation and the development of innovative therapeutic approaches.Since ANRIL is predominantly overexpressed, a potential therapeutic approach could involve reducing its intracellular abundance within cancerous lesions. This could be achieved through methods such as RNA interference (RNAi), antisense oligonucleotides (ASOs), the RNA-targeting CRISPR-Cas system, or epigenetic modulators employed to alter the epigenetic profile of the ANRIL gene . AnotherThe key role of lncRNAs in cancer development and progression has made these classes of RNA a major point of interest for researchers. As ANRIL, many are found overexpressed in cancer tissues or cancer lines in culture. Hence, the advancements in ANRIL biology should provide knowledge about its multiple activities but may also lead to the discovery of more generic activities attributed to lncRNAs.


Streptococcus agalactiae  is an important pathogen of bacterial meningitis in neonates. We aimed to investigate the clinical and genetic characteristics of neonatal GBS meningitis. All neonates with GBS meningitis at a tertiary level medical center in Taiwan between 2003 and 2020 were analyzed. Capsule serotyping, multilocus sequence typing, antimicrobial resistance, and whole-genome sequencing (WGS) were performed on the GBS isolates. We identified 48 neonates with GBS meningitis and 140 neonates with GBS sepsis. Neonates with GBS meningitis had significantly more severe clinical symptoms; thirty-seven neonates (77.8%) had neurological complications; seven (14.6%) neonates died; and 17 (41.5%) survivors had neurological sequelae at discharge. The most common serotypes that caused meningitis in neonates were type III (68.8%), Ia (20.8%), and Ib (8.3%). Sequence type (ST) is highly correlated with serotypes, and ST17/III GBS accounted for more than half of GBS meningitis cases , followed by ST19/Ia, ST23/Ia, and ST12/Ib. All GBS isolates were sensitive to ampicillin, but a high resistance rates of 72.3% and 70.7% to erythromycin and clindamycin, respectively, were noted in the cohort. The virulence and pilus genes varied greatly between different GBS serotypes. WGS analyses showed that the presence of PezT; BspC; and ICESag37 was likely associated with the occurrence of meningitis and was documented in 60.4%, 77.1%, and 52.1% of the GBS isolates that caused neonatal meningitis. We concluded that GBS meningitis can cause serious morbidity in neonates. Further experimental models are warranted to investigate the clinical and genetic relevance of GBS meningitis. Specific GBS strains that likely cause meningitis requires further investigation and clinical attention. Streptococcus agalactiae  is one of the most common pathogens causing neonatal sepsis and meningitis ) gestational age and birth body weight (BBW) of the neonates with GBS meningitis were 38.0 (37.0–39.0) weeks and 2900 (2651.3–3251.3) g, respectively. The majority of cases were term-born neonates, and female patients outnumbered male patients. The patients’ demographics, clinical manifestations, and laboratory results are summarized in p < 0.001). We found that neonates with GBS meningitis had a significantly higher percentage of neurological complications  and the survivors were more likely to have neurological sequelae  at discharge than the neonates with GBS sepsis (both p < 0.001)  . The mor< 0.001) .The molecular epidemiology and genetic characteristics of the GBS isolates that caused meningitis in our cohort are summarized in All the GBS isolates of neonates with meningitis and those from neonatal sepsis were susceptible to penicillin, ampicillin, vancomycin, and cefotaxime. High resistance rates of 72.3% and 70.7% to erythromycin and clindamycin, respectively, were noted in all GBS isolates. The antibiotic resistance profiles between the GBS isolates of neonatal meningitis and those of neonatal sepsis without meningitis were not significantly different, but a specifically high antibiotic resistance rate was noted in type III (77.1–82.2%), type Ib (100%), and type V (85.7%) GBS isolates. Due to the high correlation between the serotype and sequence type, the antibiotic resistance rate to erythromycin and clindamycin was also high in the CC12 (100%) and CC17 (89.3%) GBS isolates. Additionally, most of the GBS isolates (94.9%) with resistance to erythromycin were also resistant to clindamycin.WGS was performed using three type III/ST17 GBS strains, one type Ib/ST12, and three type VI/ST-1 GBS strains, representing GBS meningitis and GBS sepsis without meningitis, respectively. Strain CP012480.1, which is also a type III/ST17 GBS isolate from neonates with invasive GBS diseases, was used as the reference strain. One V/ST1 GBS reference strain (CP010867) from NCBI was also used as the reference strain for comparison. Comparative genome analyses were performed for all GBS isolates and the two reference strains to track the possible genomics of mobile elements and insertion sequences (IS). All genes related to the component systems CovS/R, antibiotic resistance, pilus formation, capsular serotype, and virulence were investigated.Sag37, and PezT was noted in GBS isolates of neonatal meningitis, both in type Ib/CC12 and III/CC17 GBS, but not in the type VI/CC1 GBS strain. The BspC, PezT, and HvgA genes, as well as the pili, are previously reported to be associated with enhanced virulence and pathogenic mechanisms, which lead to life-threatening illness [Sag37, and BspC genes were not found in the reference CP012480.1 strain . Therefore, we highly suspected that the BspC, PezT, and ICESag37 genes were associated with the occurrence of meningitis in the neonates examined in this study.Concerning the genes of component systems CovS/R, most antimicrobial resistance genes and most virulence genes were not significantly different between the GBS strains of neonatal meningitis and those of neonatal sepsis without meningitis . However illness ,19. AddiSag37, PezT, and HvgA are summarized in Sag37 in 60.4%, 77.1%, and 52.1% of all GBS isolates that caused neonatal meningitis in our cohort. Additionally, HvgA was present in 80% of the type III/CC17 GBS isolates of neonatal meningitis.PCR was performed for all type Ib and type III GBS isolates to verify the results and confirm the presence of multiple genes in all clinical GBS isolates. The primers used for all targeted genes, including BspC, ICESag37, to be potentially associated with the occurrence of GBS meningitis. We highlight the requirement of further in vivo and in vitro studies to document the roles and underlying mechanisms of these genes, which can be the future bases of preventive and therapeutic strategies.Compared to numerous studies that have investigated the molecular epidemiology of neonatal GBS invasive diseases and colonization in pregnant women, relatively fewer studies have focused on GBS meningitis ,21,22. WIn our institute, we have applied the policies of routine GBS screening for pregnant women and intrapartum antibiotic prophylaxis (IAP) since 2004. A change in policy was associated with the predominance of serotype III/ST-17 GBS isolates since 2010 and the increasing prevalence of antibiotic-resistant GBS isolates ,13,20,25Various virulence genes and mechanisms have been documented to be associated with the occurrence of GBS meningitis in neonates, such as the enhanced penetration of the BBB caused by HvgA genes, pili, and Srr1/Srr2 genes ,29,30,31Sag37, carrying multiple virulence genes and replacing the PI-1, is correlated with the clonal expansion of antibiotic-resistant type III/CC17 GBS strains, which may be associated with the occurrence of meningitis [Pili are known to be associated with enhanced cell adhesion of GBS isolates, which contributes to transcytosis of the endothelium and increased penetration of the BBB. Therefore, the specific pilus profiles are reported to be associated with sepsis and meningitis ,31. Prevningitis .Streptococcus pneumonia and is part of the epsilon/zeta system (the PezA/T system), which is a special toxin/antitoxin system that protects the bacterium itself [The PezT gene was initially found in m itself ,39. PezTm itself ,39. The m itself ,39. In cm itself , we deteAn obviously decreased GBS sepsis-attributable mortality rate has been reported in recent decades, from more than 20% in the 1980s to approximately 10.7% in recent studies ,5,41,42.TM SMRT  and MiSeqTM  sequencing technologies for WGS, which have the advantages of easy analysis, few errors, relatively lower cost per sample, and no specific limitations [Some limitations in this study should be addressed. All the invasive GBS isolates in this study were from a single center in Taiwan over an extensive period of time. During the prolonged timeframe, the therapeutic and IAP policies may have been changed. Therefore, a multicenter study is needed to investigate the epidemiological and genetic trends of GBS strains that cause neonatal meningitis across different regions and time periods. We applied the PacBioitations ,44,45. HSag37 genes, which carries many specific virulence profiles and multiple antibiotic resistance genes, are potentially associated with the enhanced penetrative ability of GBS isolates to cross the BBB and the occurrence of meningitis. Therefore, further in vitro and in vivo models to investigate the roles of these genes on the occurrence of GBS meningitis and the relevance of clinical features are highly suggested. Given such genetic specificity and clinical importance, a multicenter study with more cases of GBS meningitis and GBS isolates is warranted to investigate the molecular mechanisms involved, and the continuous monitoring of neonates with GBS sepsis is critically important in the future.In conclusion, GBS meningitis in neonates is associated with a higher severity of illness and a high risk of neurological sequelae. GBS meningitis is most likely caused by type III/CC17 and type Ib/CC12 GBS isolates. Genetically, the HvgA, BspC, PezT, and ICEWe conducted a single-center cohort study that enrolled all neonates with GBS meningitis who were hospitalized in the NICUs of Linkou Chang Gung Memorial Hospital (CGMH) between 2003 and 2020. Linkou CGMH is the largest tertiary-level medical center in Taiwan and located in North Taiwan. There are three NICUs with a total capacity of 47 beds equipped with ventilators and 55 beds in special care nurseries in Linkou CGMH. Meningitis was defined based on the standard criteria ,47 and aWe defined severe sepsis, septic shock, and uncomplicated bacteremia based on the definitions of our previous studies and the Centers for Disease Control and Prevention ,48. The adhP, atr, glcK, glnA, pheS, sdhA, and tkt) were based on a standard protocol described in our previous publication [Streptococcus agalactiae MLST database . All GBS isolates can be clustered into several major clonal complexes (CCs) based on the goeBURST program [adhP) as the housekeeping locus and sag647, sag1406, and san1517 for PI-1, PI-2a, and PI-2b, respectively.The capsular serotypes of all GBS isolates were analyzed using the multiplex PCR assay to identify the GBS isolates of types Ia to IX. The DNA isolation method and the PCR assay that amplified and sequenced seven housekeeping genes  [TM  [Three GBS isolates selected from the III/ST-17  strains, one type Ib/ST-12 (termed N92), and three type VI/ST-1  GBS strains were used for whole-genome sequencing (WGS). The type III/ST-17 and type Ib/ST-12 strains were obtained from the cerebrospinal fluid of neonates with meningitis, while the type VI/ST-1 GBS isolates were from the blood culture of neonates with sepsis without meningitis. The GBS isolates selected to perform the WGS were based on previous experiences and that we have found similar WGS results among the same GBS serotypes and sequence types. Additionally, type III/ST-17 and Ib/ST-12 GBS isolates were among the most common GBS strain to cause meningitis. The standard protocol of the lysozyme-sodium dodecyl sulfate-proteinase K method was used to extract DNA. WGS was performed using both PacBioCA, USA)  and MiSeCA, USA)  sequenciCA, USA)  was used2 test or Fisher’s exact test; odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Continuous variables were compared using the Mann–Whitney U test and the t-test, depending on the distribution. The trend of the proportions of categorical variables among the subgroups was analyzed via the Cochran–Armitage trend test. The results with p values of <0.05 were considered statistically significant. All statistical analyses were performed using SPSS version 23 (IBM SPSS Statistics).The clinical and genetic characteristics were compared between neonates with GBS meningitis and those with GBS sepsis but without meningitis during the study period. Categorical and continuous variables are expressed as proportions and the median , respectively. Categorical variables were compared using the χ


Missed opportunities for Human Immunodeficiency Virus (HIV) and Hepatitis C Virus (HCV) testing remain high. We aimed to ascertain the knowledge of screening guidelines and attitudes of non-infectious disease (ID) hospital physicians and assess the impact of a 1-h session on screening rates and diagnoses.This interventional study consisted of a 1-h training session on HIV and HCV epidemiology and testing guidelines for non-ID physicians. Pre-and post-session questionnaires compared the knowledge of the guidelines and attitudes toward screening before and after the session. Rates of screening and diagnoses were compared in three 6 months periods: before, immediately after, and 24 months ±4 after the session.p < 0.001), and the effect persisted until the long-term period. The HIV diagnosis rate increased globally , mainly because of medical services . The HCV screening rate increased significantly immediately and in the long term only in medical services . The new active HCV infection rates increased immediately and declined steeply thereafter.A total of 345 physicians from 31 departments participated in these sessions. Before the session, 19.9%  and 17.9%  were aware of HIV and HCV testing guidelines, respectively. The willingness to routinely test increased from 5.6 to 22%, whereas not ordering tests decreased from 34.1 to 2.4%. HIV screening rates significantly increased by 20% after the session (7.7 vs. 9.3 tests per 103 patients; A short session for non-ID physicians can improve HIV/HCV screening, increase diagnosis, and contribute to disease elimination. DelaCurrent HIV and HCV treatments are simple, safe, and highly effective in suppressing viral replication and preventing morbidity, mortality, and transmission in HIV , 5, curiHIV and HCV testing can be performed by any physician both in hospitals and primary care facilities. Missed testing opportunities in both locations remain high , althougGiven the similarity in risk factors and the population affected by both infections, we aimed to describe the knowledge on screening policies and the current routine HIV and HCV screening practices of non-ID physicians in a tertiary hospital and to assess the impact of a 1-h training session on screening rates of both infections.2.We conducted an interventional study at Ramón y Cajal Hospital in Madrid, Spain, a tertiary hospital with 900 beds and 32 medical and surgical departments covering a population of approximately 600,000 inhabitants. From February to November 2019, five physicians  provided a non-mandatory 1-h training session on HIV, HCV, and other sexually transmitted infections, including epidemiology, risk factors, clinical aspects, and screening, to the other 31 hospital departments  pandemic, the normal workflow of each department has changed. To avoid possible biases due to modifications in the number of attended patients and serologies requested by each specific department, we decided to censor follow-up for those departments that had not completed their post-training observation period by March 14, 2020. For each department, the number of evaluated days before the session was matched to that in the post-training and long-term periods .p values <0.05 were considered statistically significant. Statistical analysis was performed using Stata® 17.0 software  and Microsoft® Excel 16.67 software . GraphPad Prism 9 (GraphPad Software Inc.) was used to generate the figures.We used medians and interquartile ranges (IQR) for descriptive analysis, Fisher’s exact test to compare categorical variables, and Wilcoxon’s signed-rank test to compare paired quantitative variables. All contrasts were bilateral, and 3.The training sessions included 345 physicians from 31 hospital departments, with a median of 16 physicians per department (IQR 6–23). They were mainly women (59.3%) and under 40 years of age .3.1.p < 0.001) and 17.9%  of the participants were aware of HIV and HCV testing guidelines, respectively. When asked whether they ordered these tests during their usual practice, 5.6% claimed to ask for them routinely , 60.4% if there were identifiable risk factors and/or indicator conditions , and 34.1% never ordered them , and a significant decline in those who did not consider ordering a test . In addition, 98.2% of the participants  considered the training session and potential availability of a tool to guide HIV and HCV testing. Regarding the post-training questionnaire, we observed a significant increase in physicians who showed a positive attitude toward routinely asking for screening tests . This increase was significant both in medical and surgical departments , but this increase was only seen in medical departments , Preventive Medicine Department , and Nephrology   .3 attended patients, p < 0.001) with a significant increase in the screening rate in medical departments  but not in surgical departments   driven only by medical departments  (Table S5b), and a significant decrease in the rates of HCV active infection found  90% goals in 2020, coming short only in the diagnostic goal , 29; TheThus far, no strategies based on training sessions to hospital physicians had demonstrated effectiveness up to now , 31, 32.Despite the hardwearing effect that can be seen in our study, the impact of a new session, months after the first session, focusing on the lack of each service, could be used to assess whether periodic training could help to further reduce the burden of hidden infections.Our study has some limitations. First, the COVID-19 pandemic struck before the post-session observation was completed in some departments, so we were obliged to reduce the pre-and post-observation periods in some departments; however, it also disturbed the standard workflow up to the present day , and altIn conclusion, a formative session for hospital physicians can improve HIV/HCV screening and diagnostic rates and therefore help eliminate both infections, although other strategies should also be explored. Different strategies to tackle surgeons’ and other medical specialties’ reluctance to test must be added to the formative session to automatize and personalize the different strategies in each department. More extensive multicenter studies are needed to confirm these results.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.ceic.hrc@salud.madrid.org) with ID number 001-23. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.The studies involving human participants were reviewed and approved by Ethics Committee at University Hospital Ramón y Cajal  through the project PI22/01878, the Spanish Ministry of Science and Innovation and co-funded by the European Union.AG-R reports personal fees for educational events and non-financial support, including support for attending meetings and/or travel from ViiV Healthcare, Gilead Sciences, Merck Sharp & Dohme and Abbvie, outside the submitted work. JM-S reports personal fees for presentations or educational events and non-financial support, including support for attending meetings and/or travel, from ViiV Healthcare, Janssen Cilag, Gilead Sciences, Merck Sharp & Dohme and Abbvie outside the submitted work. MV-G has received honoraria  as a speaker in educational programs sponsored by ViiV, and Gilead; and has received support  for expert courses and congresses by MSD and ViiV. BR-H has received funding to attend conferences from Gilead Sciences and obtained a financed project from Roche. SM has been involved in speaking activities and received grants for research from Gilead Sciences, Janssen Cilag, Merck, Sharp & Dohme and ViiV Healthcare. MP-E has received funding to attend conferences, educational activities or advisory, as well as scholarships from the pharmaceutical companies Gilead Sciences, Janssen, Abbvie, MSD, and ViiV. JG has received funding to attend conferences, educational activities or advisory, from the pharmaceutical companies Gilead Sciences, Abbvie, Abbott, MSD and Roche.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Soil health is the basis of ecological and food security for humanity. Among the informative indicators of soil health are microbiological indicators based on the intensity of the carbon dioxide release from the soil. The reaction of the microbial community of Haplic Chernozem Loamic, Haplic Arenosols Eutric, and Haplic Cambisols Eutric to contamination with oxides and nitrates of Ag, Bi, Tl, and Te at doses of 0.5, 1, 3, 10, and 30 derived specific permissible concentrations (SPC) was analyzed in the conditions of a vegetation experiment (the exposure period was 10 days). One derived concentration is assumed to be equal to three background concentrations of the element in the soil. The carbon content of microbial biomass in Haplic Chernozem varied between the experimental options from 6 to 218 mg/kg of soil; in Haplic Arenosols, from 3 to 349 mg/kg of soil; and in Haplic Cambisols, from 7 to 294 mg/kg of soil. Microbial biomass was a more sensitive indicator of contamination by the studied pollutants than basal soil respiration. A decrease in specific microbial respiration was found when Haplic Cambisols were contaminated with Ag, Bi, Te, and Tl oxides. Te and Tl nitrates had a significant toxic effect on each type of soil. At the maximum dose of Tl and Te nitrate, a decrease in basal soil respiration of 56–96% relative to the control and an increase in the metabolic coefficient by 4–6 times was found. The toxicity series of heavy metals averaged for all types of soils in terms of microbiological activity was established: Bi > Ag > Te > Tl (oxides) and Te > Tl > Ag > Bi (nitrates). Nitrates of the elements were more toxic than oxides. Soil toxicity due to Ag, Bi, Tl, and Te contamination was dependent on soil particle size distribution, organic matter content, and soil structure. A series of soil sensitivity to changes in microbial biomass and basal soil respiration when contaminated with the studied pollutants: Haplic Arenosols > Haplic Chernozems > Haplic Cambisols. When diagnosing and assessing the health of soils contaminated with Ag, Bi, Tl, and Te, it is advisable to use indicators of soil microbiological activity. Pollution with heavy metals and metalloids makes a significant contribution to the degradation of soil cover and the reduction of crop yields and quality worldwide. They belong to the priority pollutants. However, not all heavy metals and metalloids have been studied equally well. Environmental pollution with Hg, Pb, Cd, Cu, Zn, Cr, Ni, Mo, Co, F, B, Se, As, Mn, Ba, V, Sb, Sr, Sn, and W is more common and has been studied better ,10,11,12The main sources of Ag, Bi, Te, and Tl pollution include coal combustion plants at thermal power plants, oil refineries, and cement production ,15,16. IStudies of the ecological state of soils after anthropogenic impacts on a wide range of enzymes are rare ,33,34,35SIR). By varying the magnitude of these indicators, it is possible to draw conclusions about the state of microbial communities, which can be useful for assessing adverse anthropogenic impacts.The intensity of the basal  respiration of the soil, along with the amount of microbial biomass, is often used to characterize soil microbocenoses in different ecosystems and their response to different types of agricultural land cultivation and land use types ,49,50,51Microbiological indicators of the soil, including basal respiration, are widely used as indicators of the toxic effects of heavy metals and metalloids in contaminated soils ). An incThe impact of soil pollution with such rare heavy metals as silver (Ag), bismuth (Bi), thallium (Tl), and tellurium (Te) on the biological properties of soils has been studied differently. The effect of silver and its compounds (including nanoparticles) on biota and plants has been studied to a greater extent ,19,42,72The work objective is to assess the intensity of microbiological activity in soils contaminated by Ag, Bi, Tl, and Te.To set up a vegetation simulation experiment, soils differing in mechanical composition, organic matter content, and pH value were used. The first object of the study was Haplic Chernozems Loamic soil , selecte2O, Alfa Alesar, Haverhill, MA, USA) and silver nitrate solution . Bismuth was introduced into the soil in the form of oxide  and a solution of bismuth nitrate (Bi(NO3)3, Sigma-Aldrich, USA). Tellurium was introduced into the soil in the form of oxide  and a solution of tellurium nitrate (Te2O3(OH)NO3, Sigma-Aldrich, USA). Thallium was introduced into the soil in the form of oxide  and a solution of thallium nitrate (Tl(NO3)3, Sigma-Aldrich, USA). The compounds were introduced into the soil in terms of doses of 0.5, 1, 3, 10, and 30 SPCs (Specific Permissible Concentrations). One SPC is equal to three background concentrations of the element in the soil, since for many heavy metals, their toxicity manifests from this concentration. The calculation of the SPC is presented in Oxides and nitrates of Ag, Bi, Te, and Tl were used to pollute the soil. Silver was introduced into the soil in the form of an oxide  were added in dry form and thoroughly mixed with the soil, and nitrate samples were introduced in the form of an aqueous solution. The introduction of oxides into the soil is due to the fact that it is in this form that heavy metal compounds are mainly present in the soil. Soil contaminated with oxides and nitrates of Ag, Bi, Tl, and Te was incubated in laboratory conditions for 10 days at a temperature of 22 °C and a soil humidity of 25%.orgC) in the soil was determined photometrically—by the amount of the Cr3+ ion formed according to the method of Tjurin in the modification by Nikitin —carbon molar mass (12 g/mol), m—weight of absolutely dry soil (g), mV—molar volume under standard conditions (22.4 L/mol), and t—incubation time (h).The rate of microbial respiration  was determined by substrate-induced respiration [basalV). A glucose solution at a rate of 10 mg of glucose per 1 g of soil was carefully distributed over the soil surface. The final soil moisture after glucose application was 90% FWC. Then, 1 h after the introduction of glucose, the vials were ventilated, closed, and left to incubate at a temperature of 22 °C for 1.5–2.2 h. The rate of substrate-induced respiration (SIRV) was determined similarly to basalV. The value of microbial biomass (µg C/g of soil) in terms of carbon was calculated using Equation (2) [SIRV—rate of substrate-induced respiration (mL CO2/100 g of soil/h).The carbon of microbial biomass (piration . The mettion (2) :(2)Cmic=2) was used, which was calculated as the ratio of substrate-induced and basal respiration rates basal/VSIRV [basal/CmicV [mic/CorgC is used as an indicator of the equilibrium state of the substance in the soil, which is observed at mic/CorgC within 2–4%. The higher this index, the more organic matter is fixed in the microbial mass.To determine the functional state of the microbial community, the metabolic coefficient , and thsal/Cmic . The ratt-test) and confidence interval (95%).Statistical data processing was carried out using the Statistica 12.0 package. Statistical data  were determined, and the reliability of various samples was established using variance analysis  and contributed to its decrease at doses of 0.5, 1, 10, and 30 SPCs by 8–15% of the control, respectively. Thallium nitrate inhibited the microbial biomass of Haplic Cambisols by 20% compared to the control at a dose of 0.5 SPC. With a further increase from 1 to 30 SPCs, the inhibition of Haplic Cambisols soil biomass was 40–67% of the control, respectively.Tellurium oxide did not have a significant effect on the microbial biomass of Haplic Chernozem soil but stimulated the microbial biomass of Haplic Arenosols soil by 5–8%, showing the effect of hormesis . Tellurium nitrate inhibited the microbial biomass of Haplic Chernozem soil at doses of 3, 10, and 30 SPCs by 26, 27, and 99% of the control, respectively. In Haplic Arenosols soil, inhibition of microbial biomass was established with a dose of 0.5 SPC by 10% relative to the control. With an increase in the dose from 1 to 30 SPCs, the microbial biomass of Haplic Arenosols soil decreased by 34–100%, respectively.Thus, it was found that when measuring the basal respiration of soils, significant differences from the control were revealed at a maximum dose of 30 SPCs, while microbial biomass was affected by contamination with oxides and nitrates at a minimum dose of 0.5 SPC. Consequently, microbial biomass is a more sensitive biological indicator than the basal respiration of soils.2), the carbon fraction of microbial biomass (mic/CorgC), and the coefficient of microbial respiration (qR), in cases of contamination with oxides and nitrates, differed significantly  is directly related to the content of organic carbon in the soil. Such a trend of basal respiration in slightly humic soil has been noted previously by Terehova (2021) [mic/CorgC differed most from the control when contaminated with silver oxide and tellurium nitrate, by 43 and 35%, which was lower than in the control  . In Hapl control .mic/CorgC compounds in Haplic Chernozem soil were observed when contaminated with tellurium oxide and silver nitrate and were 12 and 9% below the control, respectively. In Haplic Cambisols soil, the ratio of mic/CorgC in comparison with the control was 52 and 69% lower when contaminated with thallium and tellurium nitrate than in the control. Less reduction of mic/CorgC compared to the control was found when Haplic Cambisols soil was contaminated with bismuth nitrate—16% lower than the control. In Haplic Arenosols soil, when the oxides of Ag, Bi, Te, and Tl and nitrates of Ag and Bi were introduced into the soil, the stimulation of the carbon fraction of microbial biomass (mic/CorgC) was 23–69% higher than in the control.The smallest changes in the ratio of According to Blagodatskaja et al. (1995), the qR value in the range of 0.1–0.2 indicates a favorable state of the soil microbial community . When qR2 coefficient both increased and decreased. The coefficient in Haplic Chernozem soil and Haplic Arenosols soil, with the exception of silver oxide, did not differ from the control. When Haplic Arenosols soil was contaminated with 30 SPCs of silver oxide, the qCO2 value was 6 times higher than in the control. In Haplic Chernozem soil, qCO2 was maximal when contaminated with silver oxide at 10 and 30 SPCs, 46 and 75% higher than the control, respectively. Haplic Cambisols soil had the highest qCO2 value, from 0.71 to 0.86, while Haplic Arenosols soil was characterized by a range of 0.14–0.20 when contaminated with Ag and Bi nitrates, and Haplic Chernozem soil was the least of all: 0.09–0.12. However, tellurium and thallium nitrates had different effects on qCO2. When Haplic Arenosols soil was contaminated with 30 SPCs of tellurium and thallium, the stimulation of the metabolic coefficient (qCO2) was 38 and 103 times compared to the control. In Haplic Chernozem soil, with contamination of 30 SPCs of tellurium, and with 10 and 30 SPCs of thallium, stimulation of the coefficient (qCO2) was found to be 4–6 times compared with the control. In Haplic Cambisols soil, the stimulation coefficient (qCO2) at a dose of 30 SPCs of tellurium was 5 times greater than in the control.With an increase in the dose of Ag, Bi, Te, and Tl, the toxicity of Haplic Cambisols soil by the qCOAccording to the Results of Microbial Biomass and Basal Respiration of Soils, the Series of Soil Toxicity were Compiled.Toxicity series of metal oxides by basal respiration:Haplic Chernozem: Bi > Te > Ag > TlHaplic Arenosols: Te > Ag = Bi > TlHaplic Cambisols: Bi > Ag > Te > TlToxicity series of metal nitrates by basal respiration:Haplic Chernozem: Tl > Te > Ag > BiHaplic Arenosols: Te > Tl > Bi > AgHaplic Cambisols: Te > Tl > Bi > AgThe most toxic effects on the basal respiration of soils of different granulometric compositions were exerted by Bi oxide and Te and Tl nitrates.Toxicity series of metal oxides by microbial biomass:Haplic Chernozem: Ag > Tl > Te > BiHaplic Arenosols: Ag > Bi = Te > TlHaplic Cambisols: Bi > Ag > Te > TlToxicity series of metal nitrates by microbial biomass:Haplic Chernozem: Te > Tl > Ag > BiHaplic Arenosols: Te > Tl > Bi > AgHaplic Cambisols: Te > Tl > Ag > BiThe most toxic effect on the microbial biomass of soils of different granulometric compositions was exerted by Ag oxide and Te and Tl nitrates. The average toxicity range for all types of soils when contaminated with metal oxides was: Bi > Ag > Te > Tl; and when contaminated with metal nitrates: Te > Tl > Ag > Bi. Microbiological indicators were the most depressed when soils of different granulometric compositions were polluted with Ag and Bi oxides and Te and Tl nitrates.A generalized series of soil resistance to changes in basal respiration and microbial biomass is presented as follows: Haplic Arenosols > Haplic Chernozems > Haplic Cambisols.Thus, the soil least resistant to Ag, Bi, Te, and Tl pollution is Haplic Cambisols soil, and the most resistant is Haplic Arenosols soil. The stability of Haplic Arenosols soil is due to its physico-chemical and physical properties  due to which metals do not pass into a fixed form and, as a result, do not have a toxic effect on soil biota such as those more heavily loamy humus soils .To assess the correlation ratio with the metal content, correlation coefficients were calculated . It was basalV if Haplic Chernozem (r = −0.59) and Haplic Arenosols (r = −0.52). Tellurium nitrate in Haplic Arenosols and Haplic Cambisols according to basalV correlation coefficient r = −0.97 and r = −0.89, respectively; a negative correlation was established with micC: Haplic Chernozems soil (r = −0.92), Haplic Arenosols (r = −0.81), and Haplic Cambisols (r = −0.54). The basal respiration and microbial biomass of Haplic Arenosols when contaminated with thallium nitrate had an inverse correlation with the metal content: r = −0.97 and r = −0.83, respectively.Oxides of all metals affected Haplic Arenosols soil contamination with negative coefficients r = −0.91, r = −0.54, r = −0.42, and r = −0.68 for Ag, Bi, Te, and Tl, respectively. Ag nitrate affected the microbial biomass of all soils with a negative correlation: Haplic Chernozem (r = −0.95), Haplic Arenosols (r = −1.00), and Haplic Cambisols (r = −0.87). Bismuth exerted an inverse correlation effect on orgC and a decrease in the rate of its mineralization [basalV means that most of the substrate is converted into CO2, and a smaller part is involved in the production of microbial biomass [2) increased by an average of 9–46% [Contamination of soils of different granulometric compositions with oxides and nitrates of Ag, Bi, Te, and Tl directly affects the basal respiration and microbial biomass of soils: with an increase in the dose of the contaminant, the response of microbiological indicators increases. Soil toxicity, due to Ag, Bi, Tl, and Te contamination, was dependent on soil particle size distribution, organic matter content, and soil structure. The influence of Ag, Bi, Te, and Tl pollution on microbial biomass is most pronounced in slightly humic soil—Haplic Arenosols. These data are consistent with the results of Terehova et al. (2021) on the assessment of microbiological indicators of highly and slightly humic soils when contaminated with Cu, Zn, and Pb . Haplic lization ,88. A de biomass ,90,91. A biomass . The addof 9–46% . Microbiof 9–46% ,94,95. Iof 9–46% . From thThe greatest sensitivity to Bi contamination was revealed by the number of bacteria and dehydrogenases activity, and the greatest informativeness by the abundance of the Azotobacter sp. bacteria (r = −0.55) and germination of radish seeds (r = −0.68) . The ordIn previous studies on the resistance of Haplic Chernozems to contamination with oxides and nitrates of Ag, Bi, Te, and Tl, it was found that when contaminated with oxides, the most informative indicators were the activity of invertase (Ag), urease , and phosphatase (Te); when contaminated with nitrates, the most informative indicators were the activity of phosphatase (Ag) and invertases  . Within 2). When diagnosing and assessing the health of soils contaminated with Ag, Bi, Tl, and Te, it is advisable to use indicators of soil microbiological activity.Contamination with Ag, Bi, Tl, and Te affects soil health, as estimated by indicators of soil microbiological activity. The toxicity series of heavy metals averaged for all types of soils in terms of microbiological activity was established: Bi > Ag > Te > Tl (oxides) and Te > Tl > Ag > Bi (nitrates). Nitrates of the elements are more toxic than oxides. Soil toxicity due to Ag, Bi, Tl, and Te contamination was dependent on soil particle size distribution, organic matter content, and soil structure. The generalized range of soil sensitivity to changes in basal respiration and microbial biomass is presented as follows: Haplic Arenosols > Haplic Chernozems > Haplic Cambisols. The most sensitive indicators of soil microbiological activity are the microbial respiration coefficient (qR) and metabolic coefficient (qCO


Global warming has caused a significant extension of the duration and the mean temperatures of summers; this is a serious stressor for dairy cows that are particularly sensitive to the high temperatures. Under these conditions, the productivity and the fertility of the animals are seriously compromised leading to considerable financial losses to the farmers. A deep understanding of the prevailing mechanisms of the cow to withstand this serious stressor, is prerequisite for planning and implementation of heat stress mitigation programs. To this end, in this review we analyze the main hormonal and molecular alterations that take place during heat stress, and which directly or indirectly affect cow’s fertility.Global warming is a significant threat to the sustainability and profitability of the dairy sector, not only in tropical or subtropical regions but also in temperate zones where extreme summer temperatures have become a new and challenging reality. Prolonged exposure of dairy cows to high temperatures compromises animal welfare, increases morbidity, and suppresses fertility, resulting in devastating economic losses for farmers. To counteract the deleterious effects of heat stress, cattl e employ various adaptive thermoregulatory mechanisms including molecular, endocrine, physiological, and behavioral responses. These adaptations involve the immediate secretion of heat shock proteins and cortisol, followed by a complex network of disrupted secretion of metabolic and reproductive hormones such as prolactin, ghrelin, ovarian steroid, and pituitary gonadotrophins. While the strategic heat stress mitigation measures can restore milk production through modifications of the microclimate and nutritional interventions, the summer fertility records remain at low levels compared to those of the thermoneutral periods of the year. This is because sustainment of high fertility is a multifaceted process that requires appropriate energy balance, undisrupted mode of various hormones secretion to sustain the maturation and fertilizing competence of the oocyte, the normal development of the early embryo and unhampered maternal—embryo crosstalk. In this review, we summarize the major molecular and endocrine responses to elevated temperatures in dairy cows, as well as the impacts on maturing oocytes and early embryos, and discuss the consequences that heat stress brings about in dairy cattle fertility. Global warming is defined as the long-lasting and steady increase in the global annual temperature. Since the early 1980s, the global temperature has been increasing by approximately 0.18 °C per decade, leading to a projection that by 2100, the Earth’s temperature could rise by 2.1 °C to 3.9 °C [The extreme summer temperatures and the annual temperature rise have been recognized as one of the gravest threats to global agriculture. The dairy sector is particularly sensitive to the climate change, as heat stress (HS) leads to increased incidences of infectious and metabolic diseases, reduced milk production, and waned fertility of lactating cows over time. When dairy cows are exposed to temperatures beyond the upper threshold of their thermoneutral zone 16 to 25 °C) , they be °C , theBehavioral changes are the initial response to HS. In this category, shade-seeking ,9 and lyIn recent decades, the preservation of high milk production during the hot months has made the implementation of cooling management systems an inextricable need. Indeed, the installation of sophisticated automated cooling systems has allowed for maintining summer milk production close to 98% of winter levels. However, strategic cooling cannot restore summer fertility, which exhibits a 30% reduction compared the winter fertility rates . This reIn all mammals, the first molecular reaction against thermal stress is the synthesis of heat shock proteins (HSPs). These proteins exert a protective role in cells by safeguarding cellular integrity, immune response, apoptosis, and contributing to post-translational folding and transportation of proteins through cellular membranes ,24,25,26HSF1 gene have been identified as possible causal variants for thermotolerance in dairy cows [It was initially believed that HSPs were exclusively associated with the response to thermal stress, but it is now clear that almost all stressors induce HSP expression . HSPs ariry cows .Among the various types of HSPs, HSP70 has garnered particular attention from the research community. At thermoneutral conditions, it is present in all cellular compartments, while during and after HS, it is predominantly present in the nucleus . It appeHSP90 genes are associated with thermotolerance, resulting in unaffected body temperature and milk yield during HS.In Zebu cattle, which are evolutionary more heat resilient than continental breeds, Sailo et al.  found thHSF1.Breed-specific responses to HS were also noted in our previous study, comparing the reproductive potential of heat-stressed oocytes from Holstein and Limousine cattle. The oocytes derived from Holstein exhibited a stronger response against HS, as evidenced by specific changes in gene expression and a higher suppression of in vitro blastocysts formation compared with Limousine oocytes. These changes may be attributed to direct or indirect selection of Holstein cattle, which are farmed in intensive systems . Diaz etDNAJC27, DNAJA4, DNAJC21, DNAJB1, and HSPA1A). These SNPs could be used for genetic selection for thermotolerance [In a recent study , we examolerance .ITPR1, ITPR2, and GRIA4) and neuroactive ligand–receptor interaction  were revealed, offering new insights for the development of genetic selection approaches. In a recent study, Luo et al. [PMAIP1, SBK1, TMEM33, GATB, CHORDC1, RTN4IP1, and BTBD7. Otto et al. [LIF, OSM, TXNRD2, and DGCR8) involved in biological processes associated with heat stress, serving as putative markers for genetic selection in Holstein cattle. Other studies utilized single gene association approaches [PRLH, SOD1, ATP1A1, and HSP70A1A. Zeng et al. [Other genetics and genomics studies have highlighted the importance of identifying loci associated with thermotolerance, which can be applied to genetic/genomic selection programs to enhance the genetic component of thermotolerance in the herds. Cheruiyot et al.  conducteo et al.  used a co et al.  used a tproaches ,42,43 ang et al.  reviewedThere is strong evidence implicating HSP70 in oocyte fertilization and early embryo development . It is wHSP90AA1 were detected between Holstein and Limousine oocytes that were subjected to moderate heat stress conditions during in vitro maturation [Cattle cumulus oocyte complexes (COCs) respond to HS by increasing the mRNA expression of HSPs during in vitro maturation when exposed to high temperatures . Hence, turation . It is wturation ,52,53,54Before the fourth cell division, early embryos are extremely sensitive to HS. At this stage, the embryonic genome is activated ,56 and tHeat stress has been long recognized as a threatening environmental condition for cattle fertility ,67,68, aThe ovarian function is regulated by an orchestrated interplay of hypothalamic gonadotrophin-releasing hormone (GnRH), anterior pituitary gonadotrophins (follicle-stimulating hormone—FSH and luteinizing hormone—LH), gonadal steroids (progesterone and estradiol), and follicular peptides . However, it is now well established that several other hormones and metabolites, such as glucose, cortisol, growth hormone, prolactin, ghrelin, and kisspeptin, affect gonadal function, acting centrally  as well as in peripheral tissues .The deviated follicular development during HS should primarily be linked to perturbated hypothalamus–pituitary–gonadal (HPG) axis communication, resulting from a vicious cycle that includes impaired gonadotrophic support  and suboptimal ovarian steroidogenesis. In many reports, the deviated HPG functionality has been examined independently of alterations occurring due to HS in the metabolic status of the animals. For example, during HS, the dry matter intake of the cows is decreased , leadingThe effects on LH concentrations and secretion pattern (pulse frequency and amplitude) after exposure of cows to HS are discrepant, with some studies reporting no effects of HS on LH concentrations ,88,89, oFurthermore, when assessing the effects of HS on LH secretion, the estrogenic status of the animals should not be overlooked. In fact, reduced estradiol levels have been reported in many studies, which can explain the negative effects of HS on LH secretion ,94,95. IIn an experiment involving controlled exposure of mature lactating cows to acute thermal stress during the winter and summer, Gilad et al.  providedSOD1 and SOD 2) and glutathione-disulfide reductase (GSR). Furthermore, the upregulation of HSP70 and HSP90, which protect cells against oxidative stress and activate self-protective mechanisms through the clearance of damaged proteins, is also observed [GRP78 and GRP94, as well as the expression of apoptotic genes (BAX and CASPASE-3) compared to control cells cultured at 37 °C [CYP11A1 and STAR [Periovulatory estradiol 17β surge is suppressed in dairy cows that are exposed to controlled HS either during the mid and late luteal phase or throughout the entire estrous cycle . Given tobserved . In vitrat 37 °C . It has at 37 °C ,107. Colat 37 °C  and/or tand STAR ,102. In and STAR ,110.While most studies agree that estradiol secretion is diminshed due to HS, this is not the case for progesterone, with the reports regarding the effects of HS on progesterone synthesis ranging from negative, neutral, or positive. When feed-restricted or normally-fed dairy heifers are exposed to moderate HS (THI = 84) for approximately 25 days (four days prior to expected estrus until the following ovulation), from day nine of the estrous cycle, progesterone concentrations are significantly lower in heat-stressed animals compared to controls or feed-restricted heifers that were kept under thermoneutral conditions . After cThe pathway of HS-induced cortisol secretion involves the initial stimulation of the para-ventricular neurons to secrete corticotropin-releasing hormone (CRH), which acts on the adenohypophysial corticotrophs, stimulating ACTH secretion and inducing cortisol synthesis by the adrenal cortex. In an early work, it was shown that within 20 min, dairy cows respond to HS by increasing cortisol secretion. If the stimulus persists, cortisol levels steadily increase and plateau at 2 to 4 h. However, under continuous (7 weeks) exposure to high temperatures, an acclimation pathway is activated, and the levels drop to suprabasal levels . The HS-It is known that stress  acts as gonadotropin secretion inhibitor, disturbing the normal ovarian cyclicity ,128,129.Prolactin is an anterior pituitary gland polypeptide hormone that is secreted from specialized cells known as lactotrophs or mammotrophs. Although initially recognized as lactation promoter , it is nThere is a consensus that HS induces over-secretion of prolactin, and it has been demonstrated that increases in prolactin secretion are positively associated with the elevation of the HS gradient ,135. ForThere is some evidence to suggest a paracrine interaction between gonadotrophs and lactotrophs in the pituitary gland. Specific interactions between these cell types have been reported in immunocytochemical studies in various species ,144, andA clear suppression of LH secretion by prolactin has been previously reported. In vivo, hyperprolactinemia has been shown to cause a significant reduction of LH release ,146, leaThe mode of action on prolactin on pituitary gonadotrophs or at the hypothalamic level remains elusive, but it is interesting to consider that in sheep, the seasonal anestrous is characterized by minimal gonadotrophin secretion and high prolactin concentrations . Hence, In cattle, follicular development, during the estrous cycle, there occurs in a wave-like pattern consisting of two, three, and rarely four follicular waves ,154,155.SOD1 mRNA in heat-stressed follicles [Although there is no conclusive in vivo evidence for the direct effect of HS on pre-antral follicles, when pre-antral follicles were individually cultured in vitro for seven days and exposed to 41 °C for 8 h daily, they had decreased viability and developmental potential compared to non-stressed controls. This decrease is likely attributed to increased oxidative stress, as revealed by the overexpression of ollicles . Preantrollicles . It is wollicles ,164. In ollicles . These fIn an in vivo study involving controlled exposure of dairy cows to HS and successive induction of follicular development through selective ultrasound-guided dominant follicle aspiration every eight days, it was reported that HS increases the number of developing small (2 to 5 mm) follicles. This increase was attributed to a possible induction of metabolites and/or growth factors by the acute HS . HoweverEarly observations by Ryan and Boland  demonstrThe effects of HS on the overall fertility of dairy cows, including disruptions in estrus expression, low conception rates, and increased embryonic mortality, have been extensively documented. However, the relative contribution of different component factors, such as oocyte quality, fertilizing capacity, gamete transport, early embryo quality, developmental competence, and the oviductal environment, can be only retrospectively evaluated, and they are extremely difficult to accurately assess.In vitro studies provide reliable and reproducible tools to study the effects of HS on oocyte and early embryo development, although the in vitro conditions can never perfectly replicate naturally occurring conditions. Despite this limitation, in vitro embryo production methodologies provide a dynamic model for the continuous study of oocytes, cumulus cells, and early embryos under well-defined environments. The following sections present data derived from findings from in vitro embryo production studies.The in vitro maturing oocyte acquires developmental competence during the first 12 h of in vitro maturation. During this time period, enhanced de novo protein synthesis occurs, and the oocyte transitions from the inductive to the synthetic phase ,172. MatHigh temperatures cause functional and structural defects in the maturing oocyte, including a failure to progress to Metaphase II ,175, earHSPA1A, HSPB11, and CCNB1). This highlights a common response to elevated temperature by key players in the cell cycle and heat tolerance , which are crucial components of the antioxidant mechanisms of the cell and have been associated also with oocyte competence [Although short-term temperature elevation during IVM does not affect HSP gene expression, the HS conditions affect the orchestrated gene expression, as revealed by the correlation coefficients, leading to a tight clustering of HSP expression with cell cycle regulatory genes  are tightly correlated under heat stress conditions [On the other hand, the short-term temperature elevation is associated with significant changes in the expression of specific genes in cumulus cells, namely nd CPT1B . This sutability . Elevatet stress . It appenditions .Oocytes respond to external thermal stimuli by increasing the production of HSPs, which act protectively by modulating protein synthesis and providing antiapoptotic functions ,188.SOD2, GPX1, BCL2) in oocytes [BCL2 and the antioxidant genes SOD2 and GPX1 in oocytes. In the resulting blastocysts, it altered the expression of the anti-toxic AKR1B1, the anti-apoptotic GPX1, HSPA1A, and BAX, and genes that regulate the stress response and contribute to embryo growth such as ATP1A1, IGF1, and TLR2 [HSPB11, SOD2, GPX1, HSP90AA1, and HSPA1A)  . TherefoDNMT1 and higher PLAC8 expression. This indicates reduced potential for epigenetic modifications and an increased likelihood of developing a well-functioning placenta, respectively [It is noteworthy that embryos produced from heat-stressed oocytes exhibited particular characteristics. For example, compared to untreated controls, these embryos presented lower ectively , which aectively .HSPA1A and HSP90AA1 after exposure of early morulae to a 1.5 °C temperature elevation (40.0 °C instead of 38.5 °C) for 2 to 8 h [As stated above, the early embryo is extremely vulnerable to high temperatures and exhibits biphasic thermosensitivity. It is highly thermosensitive until the early morula stage, after which thermotolerance gradually increases ,50,51. A2 to 8 h . In a re2 to 8 h . In conjDespite achieving thermotolerance, the embryos remain sensitive to thermal insults. This sensitivity is evident in the increased occurrence of late embryonic losses during the summer, which could reach up to 40% of confirmed conceptions . We haveThe oviduct plays a significant role in mammalian reproduction, as it is involved in gamete transport, fertilization, mother–embryo crosstalk, and migration of the embryo into the uterus. The oviduct accomplishes most of its roles through the oviductal fluid, which contains carbohydrates, proteins, salts, metabolite lipids, and extracellular vesicles (EVs) . The comThe mother–embryo crosstalk begins at the oviductal environment and is essential for proper development of the embryo, as it enhances the embryo’s heat stress resistance, its gene expression, and its developmental potential . This cIn a recent study , a compaIn recent years, there has been a growing interest in decoding the role of the oviductal extracellular vesicles in embryo development and maternal–embryo communication. Extracellular vesicles (EVs) are membrane-enclosed nanoparticles that are detected in the extracellular space, containing proteins, lipids, mRNAs, and miRNAs cargo ,204. EveIt is undeniable that HS is undermining cow productivity and fertility, directly jeopardizing the viability and sustainability of the global dairy industry. The mechanisms through which HS disrupts the endocrine, reproductive, immune systems, and metabolism of cows are only partially understood due to their complexity and multiple interactions. Advanced nutritional interventions aiming to increase dry matter intake and improve feed utilization, as well as hormonal treatments targeting improved conceptions per insemination, somewhat mitigate the situation but fall short of solving the problem. While widely used techniques and technologies for cooling the cows and regulating the microclimate of the farms address the problem of low summer productivity, they have negligible effect on subfertility. Additionally, their high installation and operational costs, as well as their significant environmental footprint, indicate that their effectiveness must be meticulously re-evaluated. It appears that a solution to the problem should be pursued through the combination of science and technology. The vision of creating a new cattle population with inherent increased thermal resilience, requiring only moderate artificial cooling, is not unrealistic. This population could be developed through intense selection for thermotolerance ,207 and


This study harnessed machine learning to forecast postoperative mortality (POM) and postoperative pneumonia (PPN) among surgical traumatic brain injury (TBI) patients. Our analysis centered on the following key variables: Glasgow Coma Scale (GCS), midline brain shift (MSB), and time from injury to emergency room arrival (TIE). Additionally, we introduced innovative clustered variables to enhance predictive accuracy and risk assessment. Exploring data from 617 patients spanning 2012 to 2022, we observed that 22.9% encountered postoperative mortality, while 30.0% faced postoperative pneumonia (PPN). Sensitivity for POM and PPN prediction, before incorporating clustering, was in the ranges of 0.43–0.82 (POM) and 0.54–0.76 (PPN). Following clustering, sensitivity values were 0.47–0.76 (POM) and 0.61–0.77 (PPN). Accuracy was in the ranges of 0.67–0.76 (POM) and 0.70–0.81 (PPN) prior to clustering and 0.42–0.73 (POM) and 0.55–0.73 (PPN) after clustering. Clusters characterized by low GCS, small MSB, and short TIE exhibited a 3.2-fold higher POM risk compared to clusters with high GCS, small MSB, and short TIE. In summary, leveraging clustered variables offers a novel avenue for predicting POM and PPN in TBI patients. Assessing the amalgamated impact of GCS, MSB, and TIE characteristics provides valuable insights for clinical decision making. Traumatic brain injury (TBI) is a global health concern that exacts a heavy toll on lives and well-being across all age groups. The grim statistics reveal its pervasive impact: approximately 4.48 million people have succumbed to trauma-related incidents, constituting 8% of all global fatalities, with TBI accounting for around 2 million of these tragic deaths ,2,3. Of 2 and CO2 between atmospheric air and the bloodstream is a vital process. Remarkably, the brain, which is the body’s oxygen powerhouse, orchestrates this exchange, with blood CO2 levels playing a pivotal role. However, lung function is intricately linked to cerebrovascular health [The intricate web of connections between the brain and various bodily organs, including the central nervous system and the gastrointestinal tract, is well documented and commonly referred to as the “brain–gut axis” ,5. Recenr health  and can r health .Numerous factors exert a profound influence on the outcomes of TBI cases. Swift transfers to healthcare facilities equipped with neurosurgical expertise have been associated with more favorable prognoses . The sevMachine learning (ML), a subfield of artificial intelligence (AI), possesses the prowess to analyze copious amounts of patient data, discern intricate patterns, and provide accurate predictions and risk assessments—a capability with far-reaching implications in the medical landscape, including predictive analytics ,20,21,22This retrospective study aimed to predict and analyze the impact of three important factors on postoperative mortality and postoperative pneumonia (PPN) in patients undergoing surgical intervention for TBI: the time from injury to hospital admission, the severity of TBI as measured by GCS score, and midline shift of the brain. Additionally, this study investigated the impact of clustering variables derived from these factors on prediction and outcome. The analysis included patient cohort data from January 2012 to July 2022.Prior to commencement, this study received approval from the Clinical Research Ethics Committee of Chuncheon Sacred Hospital (IRB No. 2022-09-007). All research activities adhered strictly to the ethical principles outlined in the Helsinki Declaration. Given that the clinical data utilized were obtained after the conclusion of patient treatment, informed consent was waived for this study, which involved vulnerable participants, specifically individuals with traumatic brain injuries.The primary outcomes for this study included postoperative mortality (POM) and postoperative pneumonia (PPN). POM was defined as death occurring prior to discharge following surgery, while PPN was diagnosed based on the presence of pneumonia or pulmonary infiltrations evident in chest X-rays, coupled with fever (>38 °C), abnormal white blood cell counts , or positive blood culture findings.Data were sourced from the clinical data repository of Hallym University Medical Center, encompassing data from five hospitals collected over a decade. This comprehensive database houses an array of medical records, prescription information, laboratory test results, and radiological findings. Its extensive data repository allows for the retrieval and analysis of patient data, considering demographic attributes, prescriptions, diagnostic examinations, and treatment details. In addition to structured data, the clinical data repository also contains unstructured textual information, including patient notes, test results, and medication administration records. Several previous studies have successfully utilized data from this repository ,28,29,30This study included patients who had undergone surgical intervention for TBI and met the criteria for prediction and analysis as described above. Patients with preoperative pneumonia or incomplete data were excluded from the analysis.2); alcohol and tobacco use; imaging findings; presence of skull fracture; various types of hemorrhage ; American Society of Anesthesiologists physical status; blood and fluid volumes administered during surgery; urine output during surgery; estimated blood loss during surgery; packed red blood cells transfused during surgery; fresh frozen plasma transfused and platelet count; preoperative blood urea nitrogen, creatinine, albumin, sodium, and potassium levels; total duration of ventilator support; ratio of arterial oxygen partial pressure to inspired oxygen fraction (PO2/FiO2 < 300); and the nature of the initial surgical procedure .The main predictors in this study included three essential variables: the time between injury and emergency room (TIE) admission (days), the Glasgow Coma Scale (GCS) score, and the size of midline brain shift (mm), all derived from evaluations performed in the ER. In addition to the above three, we considered a comprehensive set of 33 features, including patient attributes and pre- and postoperative data, to enable early prediction. These included age; sex; surgery duration; obesity , a widely recognized metric in machine learning and feature selection tasks ,41,42. ITo generate a novel predictor, we adopted the agglomerative (bottom-up) hierarchical clustering technique, utilizing the primary predictors as the foundation for the clustering process. Agglomerative hierarchical clustering employs a bottom-up methodology, whereby clusters encompass sub-clusters that progressively branch into additional sub-clusters, thereby constructing a hierarchical structure. The process commences by assigning individual objects to distinct clusters. Subsequent iterations involve the amalgamation (merging) of the closest pair of clusters based on specific similarity criteria. This iterative procedure persists until all data points are encompassed within a single cluster . We analIn the realm of descriptive statistics, continuous features were represented by medians alongside interquartile ranges, while categorical features were denoted by numbers accompanied by corresponding percentages. Disparities between groups were expressed as absolute standardized differences. To assess the relationship between clustering and POM as well as PPN, odds ratios and their corresponding 95% confidence intervals were computed through logistic regression. Adjusted odds ratios were obtained using backward elimination. Statistical analyses were conducted using SPSS statistical software . Hypothesis testing was conducted in a two-sided manner, with a Bonferroni correction applied .In the current study, a total of 720 patients were initially considered for inclusion, and 103 patients were subsequently excluded as outlined in The dataset for training comprised 432 patients, while the test dataset included 185 patients. The performance metrics for predicting POM and PPN are summarized in The first evaluation included a total of 36 features listed in In the context of predicting both POM and PPN, the Glasgow Coma Scale (GCS) exhibited rankings ranging from 7 to 11 for POM and 2 to 25 for PPN. Similarly, the TIE displayed rankings between 10 and 16 for POM and 10 and 18 for PPN. Furthermore, the midline shift of the brain (MSB) revealed rankings spanning from 20 to 33 for POM and 18 to 30 for PPN. For a comprehensive overview of the importance of GCS, TIE, and MSB with respect to the prediction algorithms for POM and PPN, please refer to The dendrogram, akin to The number of patients in each of clusters 0 to 4 was 148, 11, 206, 126, and 126, respectively. The number of deaths in each of clusters 0 to 4 was 19 (12.8%), 0 (0%), 20 (9.7%), 49 (38.9%), and 53 (42.1%), respectively. The number of patients experiencing PPN in each of clusters 0 to 4 was 47 (31.8%), 0 (0%), 34 (16.5%), 47 (37.3%), and 57 (45.2%), respectively.Cluster 0 predominantly comprises individuals with high GCS scores ranging from 10 to 15, accompanied by low MSB values spanning from 0 to 7.1 and minimal TIE ranging from 0 to 5.Cluster 1 is characterized by elevated GCS scores, ranging from 8 to 15, yet exhibits variability in both MSB (ranging from 0 to 18.1) and TIE (ranging from 27 to 64).Cluster 2 generally consists of individuals with high GCS scores, ranging from 8 to 15, paired with low TIE values ranging from 0 to 21. However, the MSB values in this cluster vary between 2.0 and 23.3.Cluster 3 is distinguished by low GCS scores spanning from 3 to 10, coupled with low MSB values ranging from 0 to 9.7 and minimal TIE ranging from 0 to 5.Cluster 4 features individuals with low GCS scores (ranging from 3 to 10), minimal TIE values (ranging from 0 to 1), and notably high MSB values, which fall within the range of 6.5 to 31.9.The following is an analysis of the characteristics observed within each cluster:The model including clustering features was evaluated with 34 features, excluding GCS, TIE, and MSB. The prediction results of POM and PPN with clustering results added as features in addition to the basic features are summarized in Within the context of predicting both POM and PPN, the cluster feature exhibited rankings within the range of 5 to 24 for POM and 14 to 37 for PPN. Further details on feature importance, categorized by prediction algorithm for both POM and PPN, can be found in In our analysis, the cluster variable was employed as a categorical factor in logistic regression. Within this framework, we designated cluster 0 as the reference group, distinguished by generally high GCS scores, minimal time elapsed between ER admission and trauma, and limited midline brain shifts. Cluster 3 had a 3.2-fold higher risk of POM than cluster 0. The findings, comprising both unadjusted and adjusted odds ratios for the cluster variable with respect to occurrences of POM and PPN, are succinctly presented in In this study, we used machine learning to predict postoperative mortality and pneumonia in patients undergoing surgery for TBI. We also investigated the impact of GCS score, MSB, and TIE on prediction. Additionally, we investigated the impact of new variables that cluster these variables on the prediction and risk of POM and PPN. Our analysis included data from a large patient cohort of 617 patients from January 2012 to July 2022. We observed that 22.9% of patients experienced postoperative mortality, while 30.0% experienced postoperative pneumonia. According to the algorithms, the AUCROC for POM and PPN prediction before including the cluster variables was 0.63–0.72 and 0.68–0.78, respectively, and after including the cluster variables, it was 0.46–0.75 and 0.65–0.80, respectively. When calculating the odds ratio according to the new cluster variables, cluster 3  had a 3.2-fold higher risk of postoperative mortality than cluster 0 .Abujaber et al., reported the prediction of in-hospital mortality in patients with TBI . They acOur findings follow previous studies highlighting the importance of rapid transfer to a hospital with neurosurgical facilities, high GCS score, and small midline shift of the brain in achieving better outcomes for TBI patients. ,51,52. TAlthough we can know the importance of variables in the prediction of machine learning, we cannot know how the variables work in some studies ,53. EvenThis study has the following strengths: First, it provides a foundation for the outcomes by analyzing a significant amount of patient data from 617 patients over a period of 10 years through comprehensive data analysis. Second, it is partially consistent with previous studies that emphasize the importance of factors such as rapid transportation to the emergency room, GCS score, and midline shift of the brain, which has clinical implications for the management of TBI patients. Third, it shows the potential of AI and ML in the medical field by using machine learning algorithms to predict POM and PPN. Fourth, it is innovative to use clustering to generate new variables based on the time from injury to the emergency room and the severity of TBI. This approach allows for a more nuanced understanding of patient subgroups and related risks. However, this study has several limitations. First, this study is retrospective, meaning it relies on past data. This design can introduce bias and limitations related to data quality and the uncontrollability of variables. Second, the study results are based on data from a specific patient population within a specific healthcare system. As healthcare practices and patient demographics can vary, it may be difficult to generalize the results to a wider population. Third, this study uses multiple machine learning algorithms, but the algorithm selection can affect the results. The performance of an ML model can vary depending on the specific algorithm selected. Fourth, external validation of the prediction model on a separate dataset is essential to assess the generalizability and robustness of the results. This study does not mention external validation. Fifth, we did not explicitly determine cutoff values for feature importance, and we used all available features for model development. We are aware of the limitations of the cutoff value determination process. It is important to note that it can be difficult to determine optimal cutoff values for individual algorithms, and there may not be a single solution that applies to all cases. However, feature optimization is needed for performance improvement in future studies.This study harnessed the power of machine learning to predict POM and PPN in patients with TBI. Utilizing the GCS score, MSB, and TIE as foundational predictor variables, we embarked on an innovative predictive journey by incorporating clustered variables. These newly derived variables, crafted through advanced clustering techniques, encapsulate critical information from the original predictors, enabling a comprehensive assessment of the amalgamated risk posed by multiple contributing factors within each patient. For future investigations, it is imperative to broaden the scope by encompassing data from diverse healthcare institutions, thus enhancing the generalizability of our clustering-derived variables through rigorous external validation processes. This collaborative effort will advance our understanding of postoperative outcomes in TBI patients and contribute to more effective clinical decision making.