Freezing phenomenon is a striking feature of Parkinson’s disease. However, it has never been studied in people with dementia with Lewy bodies (DLB). We designed a freezing of speech single questionnaire (FOSSQ) and investigated the frequency and association of freezing of speech (FOS) in patients with DLB and other types of dementia.This is a retrospective analysis of data from the project of history-based artificial intelligent computerized dementia diagnostic system. We compared the frequencies of FOS among non-demented (ND) participants, patients with Alzheimer’s disease (AD), vascular dementia (VaD), and DLB. Further, we explored the association factors of FOS in all the participants.p < 0.001). The association factors of FOS were older age, more severe dementia, more severe motor dysfunction, fluctuating cognition, visual hallucinations, parkinsonism, rapid eye movement sleep behavior disorder, attention, mental manipulation, and language.We enrolled 666 individuals with the following disease distribution: 190, ND; 230, AD; 183, VaD; and 63, DLB. Compared to individuals with ND (2.1%), patients with AD (6.1%), or VaD (18.0%), DLB (54.0%) showed a significantly higher frequency of positive FOS (all Our study showed that the informant-based FOSSQ may be a practical screening tool for discriminating DLB from individuals with ND or other forms of dementia. The FOSSQ can be applied in clinical practice as well as on the artificial intelligent platform. Freezing phenomenon is a striking feature of Parkinson’s disease (PD). Freezing of gait (FOG) is a form of akinesia and is one of the most disabling symptoms of PD . PreviouTo study FOS, we designed a simple informant-based FOS single questionnaire (FOSSQ) that was embedded in the History-based Artificial Intelligent Clinical Dementia Diagnostic System (HAICDDS) . We aimeThis is a sub-study of the project of HAICDDS, which is currently used as a registration platform in the Show Chwan Healthcare System . ParticiA diagnosis of AD was made according to the criteria for dementia due to AD developed by the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for AD . A diagnND participant was diagnosed with a global CDR  score ofThis is a retrospective analysis of data from the HAICDDS, which is currently applied in three centers in Taiwan . Daily function was assessed using the Instrumental Activities of Daily Living (IADL) Scale . Cognitipost hoc analysis according to the homogeneity of variance. Between-group comparisons of demographic and background characteristics with positive FOSSQ (FOSSQ +) and negative FOSSQ (FOSSQ-) were adjusted for age and disease severity. Comparisons of each cognitive domain in the CASI between FOSSQ + and FOSSQ- groups were adjusted for age, gender, and disease severity. Comparison of frequency of symptom fluctuation in the Mayo Fluctuation Composite Score (MFCS) and language symptoms in the HAICDDS-Language between FOSSQ + and FOSSQ- groups were adjusted for age and disease severity.The Chinese version of SPSS 22.0 for Windows  was used for statistical analyses. Between-group comparisons of demographic data, neuropsychological tests, CDR sum of boxes (CDR-SB), IADL score, MoCA score, CASI score, NPI total score (NPI-sum), motor subscale of the Unified Parkinson’s Disease Rating Scale (UPDRS-M), and DLB features were analyzed using independent t-test or one-way ANOVA with either Bonferroni or Dunnett T3 The participants were selected from a registry-based database of the Show Chwan Health System. The study design was retrospective, and the data were anonymously analyzed. The Committee for Medical Research Ethics of Show Chwan Memorial Hospital reviewed the project and the Data Inspectorate approved the study.p < 0.001). Comparison of demographic and background characteristics among patients with ND, AD, VaD, and DLB revealed significant differences in all the parameters (p < 0.001), except for informant age or education , AD (6.1%), or VaD (18.0%), those with DLB (54.0%) showed a significantly higher frequency of FOSSQ +  except for gender, education, and history of cerebrovascular accident . After adjustment for age and disease severity, the FOSSQ + group had significantly lower scores only in the attention and language domains  . Given oAs a sub-study of the project HAICDDS, we analyzed data from a relatively large population  and obtained important results. First, we successfully demonstrated that even a single question could provide good discrimination among dementia stages and subtypes. The frequencies of FOSSQ + in patients with CDR scores of 0, 0.5, 1, 2, and 3 were 3.8, 3.0, 15.2, 26.1, and 43.5%, respectively, indicating that the frequency of FOS significantly increased with increase in dementia severity. Regarding discrimination of dementia subtypes, the FOS phenomenon was found more often in patients with DLB (54.0%) compared to those with either AD (6.1%) or VaD (18.0%). The frequency of FOSSQ + was lower among females, which is consistent with previous findings on freezing phenomenon in Parkinson’s disease .p < 0.001), including cognitive fluctuations (OR = 10.22), visual hallucinations (OR = 4.18), parkinsonism (OR = 4.68), and REM sleep behavior disorder (OR = 2.84). Motor dysfunction according to the UPDRS-M is more severe in the FOSSQ + group . Therefore, it is reasonable to consider FOS as part of the characteristic features in the diagnosis of DLB than that of ND or other types of dementia. The dopamine transporter imaging is not significantly different after adjustment, however. We proposed that non-DLB participants who performed dopamine transporter imaging were comorbid with motor dysfunction and were highly suspected to have parkinsonism. Hence, a relatively high percentage of DaTabN was found among these patients (47.1 and 20% in VaD and AD). Our next piece of research to address this issue and to clarify the association of FOS and dopamine transporter uptake in the striatal area is underway.Second, the frequency of FOS was not only highest in patients with DLB but was also more significantly associated with DLB features (Third, after adjustment for age and dementia severity, the FOSSQ + demonstrated a strong relationship with dysfunctions in attention, mental manipulation, and language domains in the CASI. This indicated that FOS might be a combination of these types of cognitive dysfunction, which is consistent with our hypothesis. Some researchers may argue that using the cognitive screening tool CASI, attention domain screens only registration and repetition and language domain screening cognitive performance on reading, writing, naming, and comprehension. Whether attention and language subscales in CASI represent the actual functions of attention and language might be controversial based only on a cognitive screening tool. Further detailed and specific neuropsychological tests to clarify the association of FOS with cognitive functions will be necessary.Fourth, it is consistent with our recent findings of language dysfunction being more severe in patients with DLB than those with other dementia using the HAICDDS-Language questionnaire . In the Finally, in the HAICDDS project, we have continuously provided evidence that the combination of multiple standardized and structured questions is more powerful and can be optimized using artificial intelligence with machine learning techniques. This was the original idea behind the design of the HAICDDS questionnaire.This study has several limitations. First, we used a single questionnaire for detecting FOS in patients with DLB and other forms of dementia; therefore, further studies using more tools are required to confirm the findings. Second, we used the original Taiwanese version of the questionnaire, which, although we translated to English, would call for a more precise and colloquial translation. Third, our study was conducted in only three centers in Taiwan. Therefore, the findings on the prevalence and association factors of FOS are not generalizable to all patients with DLB or other types of dementia.In conclusion, our study showed that the informant-based single questionnaire FOSSQ can be a practical screening tool for the discrimination of DLB from ND or other types of dementia. The FOSSQ can be applied in clinical practice as well as in the dementia registration platform. Further machine learning techniques using artificial intelligence could improve the accuracy and efficiency of the questionnaire.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.The studies involving human participants were reviewed and approved by the Show Chwan Memorial Hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.P-YC undertook the literature search and data analysis, edited the Author Contributions, and was mainly responsible for revisions and drafts of the manuscript. M-CP participated in the data analysis and contributed to revisions and the final draft of the manuscript. G-UH undertook the literature search and contributed to revisions. C-YW contributed to revisions of the manuscript. R-CT undertook the literature search and contributed to revisions.P-YC work has been partly supported by the Show Chwan Memorial Hospital. 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.


Ziziphus spina-christi leaf on digestive fermentation, antioxidant status, and haemato-biochemical indices of growing rabbits. Ziziphus leaves (ZLs) of Ziziphus spina-christi were collected from Sidr trees scattered throughout the city of Medina, Saudi Arabia. Three formulated diets containing 0, 10, and 20 g Ziziphus spina-christi/Kg diet as supplementation were offered ad libitum. The organic matter content of ZL was higher. Chemical compositions were comparable in all of the contents of the tested diet. Quantities of gas released from the control diet were higher, and then the gases released decreased significantly (P < 0.05) with the addition of ZL. The values of NH3-N were taken as the same trend. The addition of a high level of ZL to rabbit diets led to a decrease (P < 0.05) in the total count of bacteria as well as the number of E. coli and Clostridium spp. However, the number of Enterococcus bacteria was not affected by supplementation. Haemoglobin parameters of the control group and groups 2 and 3 were compared: white blood cell count and red blood cell count. These observations of total protein and albumin within the range of reference values were reported in healthy rabbits, while glucose significantly decreased with the addition of ZL and AST in the blood increased significantly. The values of TP, albumin, and ALT measurements showed no significant differences among groups fed on test diets. Significant differnces in serum immunoglobulins were observed between the groups, while the high levels of ZL supplement led to a significant (P < 0.05) increase in the serum IgA, IgG, and IgM levels. Antioxidants expressed as T-AOC, GSH-Px, T-SOD, and CAT in the blood of animals fed on diets containing high levels of ZL were significantly higher. Higher serum T-AOC, T-SOD, and CAT activities were observed in rabbits supplemented with a high level of ZL compared with the control group (P < 0.05). The supplementation of ZL tended to increase serum GSH-Px activity. The addition of ZL to rabbit diets led to an increase in dry matter intake. On the other hand, there was no significant change in the apparent digestion coefficient of DM, OM, CP, and fat. Conclusion. Dried ZL supplementation up to 20 g/Kg diet might improve the bacterial community, antioxidants, biochemical parameters and blood constituents of rabbits, and digestibility.The objective of the study was to assess the effect of diets supplemented with  Medicinal plants are used in reducing predisposition and managing diseases related to oxidative stress, such as cancer and hypertension, as they contain strong antioxidants such as flavonoids, anthocyanins, and alkaloids . Using aConsidering the continuous increase in the price of these materials, the need for improving the feeding process, the benefits of reduction of disorders caused by agitation of the gastrointestinal tract, and then the usage of these natural materials would be very useful. For resolving these problems, it is necessary to apply the growth promoters of natural extraction, which are capable of providing comparable efficacy and will not conduce to the cumulative contamination of the environment .Ziziphus spina-christi is a subtropical plant known in the Kingdom of Saudi Arabia as “Nabq” or “Sidr” which is used for various medicinal purposes. The Ziziphus leaves (ZLs) contain various alkaloids, including ziziphine, jubanine, and amphibine, alpha terpinol, linalool, and diverse saponins. Ziziphus spina-christi L has been shown to have an activity against bacteria and fungi [nd fungi .Ziziphus spina-christi in their diet. The objective of the study was to assess the effect of diets supplemented with Ziziphus spina-christi leaf on digestive fermentation, antioxidant status, and haemato-biochemical indices of growing rabbits.In this study, we set out to test the composition and functioning of the ecosystem of the digestive tract of rabbits when incorporating Ziziphus leaves (ZLs) of Ziziphus spina-christi were collected from Sidr trees in the city of Medina, Saudi Arabia. After verification and authentication, a voucher specimen was deposited in the herbarium of the Department of Laboratory Sciences, College of Sciences Medina, Taibah University. Collected leaves were washed thoroughly with tap water, rinsed again in distilled water, and then dried in shade for up to twenty days. The dried leaves were crushed to a fine powder using a blender and kept until used.The experimental work was carried out at the animal house of King Khalid Hospital, Kingdom of Saudi Arabia (KSA). ad libitum, and residues were estimated to calculate actual feed consumption. The control group was fed on a diet without ZLs (ZL0); the experimental groups were fed on the same diet with 10 and 20 g·ZL/kg . The chemical composition of the experimentally and differently tested diet is shown in ad libitum. During the whole experimental period, the feed intake was determined precisely. Feed samples were taken to analyse for proximate components using the methods of [Three formulated diets containing 0, 10, and 20 g·ZL/Kg diet as supplementation were offered thods of  for detethods of .2 in bottles and transferred immediately to the laboratory and stored at −80°C until the gut microbial composition analysis. The microbial counts  were determined according to the methods of [E. coli were identified by the methods described by [E. coliisolates was done by methods described [Blood samples were collected via anterior vena cava puncture from 5 animals selected randomly in each treatment. The samples were centrifuged at 3,000 g and 4°C for 15 min to separate out the serum and were stored at −20°C until analysis. Other blood samples were collected in EDTA tubes from the same animals for packed cell volume, total and differential white blood cell counts, red blood cell counts, and hemoglobin concentration. The samples were determined using standard laboratory procedures. At the end of trials, fresh luminal digests from five rabbits per group were chosen randomly and subjected to a stream of COthods of .E. coli ribed by . The serescribed . The serescribed . Total pin vitro gas production (GP) technique was conducted to test bacterial activities in the digestive tract of rabbits with added Ziziphus spina-christi in their diets.The 2, and then discharged immediately with special solutions under anaerobic conditions.Fresh content of the gastrointestinal tract was obtained from 10 rabbits slaughtered from the control group, subjected to a stream of COin vitro, following the methods of [Total gas production was measured by incubating tested diet with the content of the digestive tract thods of .3-N) concentration was estimated by the method of [The sample of testing diets (weight 0.2 mg) of each was incubated for 48 hours in a bottle which had a size of 100 ml after measuring GP (ml gas). Cumulative gas production was measured at 3, 6, 12, 24, 36, and 48 hr. Ammonia  methods.At the end of the experiment, digestibility trials were carried out to determine the nutrient digestibility and nutritive values. Rabbits were taken randomly (5 within each treatment). Animals were housed individually in cages (dimensions: 30 × 20 × 35 cm) that allowed the separation of feces and urine. All rabbits were kept under the same management, hygienic, and environmental conditions. The experimental diets were offered twice daily at 9 a.m. and 15 p.m., and fresh-water was provided Y is the dependent variable; μ is the overall mean of observations; a is the effect; and e is the residual error.Data were statistically analyzed using One-Way ANOVA with the means comparisons procedure . DiffereChemical compositions of testing diets are shown in P < 0.05) with the addition of ZL. Incorporation of ZL in rabbit diets caused a decrease of GP in the digestive tract due to lower fermentation of microorganisms, and this observation is in agreement with the findings of [−1). Due to the high content of ZL of condensed tannins (CTs), production of gases was affected because they inhibit the activity of bacteria in the digestive channel, and there are multiple hypotheses explain this effect. One hypothesis is that CT acts as a hydrogen sink [3-N.dings of . These rgen sink . Anothergen sink . A thirdP < 0.05) in the total count of bacteria as well as the number of E. coli and Clostridium spp. These results may be attributed to that fact that tannins have general antimicrobial activities and have been reported to prevent the development of bacteria by precipitating microbial protein [Ziziphus spina-christi leaves has shown significant antibacterial activity against E. coli, in comparison with eight antibiotics. The number of Enterococcus bacteria was not affected by supplementation as shown in The addition of a high level of ZL to rabbit diets led to a decrease  in the serum IgA, IgG, and IgM level. Similar to these results, [The effects of ZL on the serum immunoglobulins are shown in results,  found hiP < 0.05). The supplementation of ZL increased serum GSH-Px activity.Ziziphus spina-christi leaf meal-containing diets did not affect DM, OM, CP, and EE digestibility.The addition of ZL to rabbit diets led to an increase in dry matter intake. The data on dry matter feed intake are presented in Under the condition of the present study, the results suggest that dried ZL supplementation up to 20 g/Kg diet might improve the bacterial community, antioxidants, biochemical parameters and blood constituents of rabbits, and digestibility. Using dried ZLs should be considered when using other industrial materials are not feasible. Future studies are needed to confirm the potential benefits of ZLs found in this study.


Molecular-based tests have the advantage of objectivity, quantification, detection of fastidious organisms, and validity for self-obtained vaginal swabs. The performance of the molecular tests against standard microscopy is impressive, but further education of users on interpretation is needed. Bacterial vaginosis is the major cause of vaginal dysbiosis and should be recognized for the threat it is to women’s genital tract health. Quantitative assessment of microbial abundance, the diversity of other organisms present, specific primers for gene sequence regions, and clades and biovars of target microbes should be recognized and incorporated into future molecular diagnostic tests to better differentiate between vaginal eubiosis and dysbiosis.“The bacterial vaginosis syndrome” has significant adverse effects for women and babies, including preterm birth and increased risk of acquisition of sexually transmitted infections and HIV. Currently, the gold standard for diagnosis is Gram stain microscopy of vaginal secretions, which is not readily available, is somewhat subjective, and does not differentiate between the likely different subtypes of vaginal dysbioses that may have different etiologies, microbiology, responses to antibiotics, and phenotypic outcomes. With new information from molecular-based, cultivation-independent studies, there is increasing interest in the use of molecular techniques for the diagnosis of bacterial vaginosis. We reviewed the current evidence on and the rationale behind the use of molecular techniques for the diagnosis of bacterial vaginosis. We found a number of commercially available molecular diagnostic tests, a few of which have US Food and Drug Administration (FDA) and/or Conformité Européenne  Lactobacilli. These organisms provide vaginal eubiosis inter alia through i) numerical dominance, ii) vaginal epithelial cell adhesion that prevents biofilm formation, and iii) killing ability over dysbiotic organisms through the production of lactic acid, hydrogen peroxide (H2O2), and naturally occurring antimicrobials such as bacteriocins2.For the purpose of this review, we have defined vaginal eubiosis as the presence in the vagina of a beneficial lactic-acid-producing microbiota, predominantly, but not uniquely, from the genus Lactobacillus-abundant/-dominant vaginal microbiota to a microbiota that has a high diversity and high abundance of potentially pathogenic organisms. This deviation may be due to the introduction of abnormal bacteria into the vagina and a paucity of Lactobacilli, particularly Lactobacillus crispatus, Lactobacillus jensenii, and Lactobacillus gasseri. This dysbiosis may be by sexual transmission of organisms such as Chlamydia trachomatis or Neisseria gonorrhoeae or by non-sexual transmission with organisms such as group A or G Streptococcus. Another possibility is that a pre-existing commensal such as Gardnerella vaginalis or Escherichia coli increases their virulence4. Finally, there may an imbalance of the normal vaginal microbiota whereby Lactobacilli are reduced in quality and/or quantity, the vaginal pH rises, and there is a 1,000-fold increase in the number of other potentially pathogenic organisms such as G. vaginalis, Atopobium vaginae, Megasphaera, Sneathia, Candidatus Lachnocurva vaginae (formerly known as BV-associated bacterium [BVAB]-1)5, Mageeibacillus indolicus (formerly known as BVAB-3)6, Mobiluncus, and other anaerobes. This latter scenario is known as bacterial vaginosis (BV) and is the commonest cause of vaginal discharge in high-income countries, responsible for one-third of all vaginal infectious morbidity worldwide, and a major cause of adverse sequelae in obstetrics and gynecology7. Historically, BV has been poorly understood because the etiology remains unknown, the microbiology differs between subjects, the response to antibiotics differs, recurrence and resistance to treatment are common, and phenotypic outcomes of the condition vary. While historically BV has been considered by some to be a mild inconvenience for women, we now know that through human immunodeficiency virus (HIV), preterm birth (PTB), and sexually transmitted infections (STIs) it carries a significant mortality and morbidity for women and babies.Vaginal dysbiosis may be defined as a prolonged deviation from a low-diversity, L. crispatus, ii) CST-II, dominated by L. gasseri, iii) CST-III dominated by Lactobacillus iners, iv) CST-V dominated by L. jensenii, and v) CST-IV, which has no specific dominant species and was termed the diverse group, characterized by a paucity of Lactobacilli and higher proportions of strictly anaerobic bacteria, including Prevotella, Dialister, Atopobium, Gardnerella, Megasphaera, Peptoniphilus, Sneathia, Eggerthella, Aerococcus, Finegoldia, and Mobiluncus. CST-IV is further subdivided into CST-IVa and CST-IVb, both of which lack Lactobacilli and contain an abundance of such anaerobes or other BV-associated organisms8. Many feel that there are numerous other dysbiotic CSTs like Ravel’s CST-IV, which may explain the poor understanding of BV with respect to different etiologies, microbiologies, responses to antibiotics, recurrence, and phenotypic outcomes9.In a cross-sectional study of 394 healthy women of reproductive age, subsequently elaborated by others, the human vaginal microbial communities were classified into five community state types (CSTs): i) CST-I dominated by 7. In gynecology, BV has been associated with urinary tract infection, pelvic inflammatory disease, post-hysterectomy vaginal cuff infection, infertility, and acquisition of bacterial, protozoal, and viral STIs, such as herpes simplex virus (HSV), human papilloma virus (HPV), and HIV. In 1955, Gardner and Dukes’ Haemophilus vaginitis (renamed BV in 1984) was considered to be nothing more than a mild inconvenience for women10, and in our experience this continues to be the case. Since 50% of women with BV are asymptomatic, this myth is perpetuated by government agencies .BV is an important condition associated with a number of adverse sequelae in both obstetrics and gynecology. In pregnancy, BV has been associated with early, late, and recurrent miscarriage, post-abortal sepsis, preterm prelabor rupture of the membranes (PPROM), postpartum endometritis, chorioamnionitis, and PTB11. Secnidazole, a new antibiotic used to treat BV, was initially rejected by the FDA for processing through the GAIN Act; however, over the last 2 years, BV has been recognized as a serious or life-threatening disease that meets the criteria for fast tracking through the GAIN Act, and secnidazole is now an approved treatment for BV in the US .In 2012, because of concerns over increasing antibiotic resistance, and as an incentive for pharmaceutical companies to develop new antibiotics, the Generating Antibiotic Incentives Now (GAIN) Act was introduced into US law as a part of the US FDA Safety and Innovation Act. This legislation extends by 5 years, from 15 to 20, the time in which antibiotics for serious or life-threatening infections can be sold without generic competition. The GAIN Act grants fast-track and priority review status as well as an expedited regulatory approval process to qualifying drugsP = 0.028), indicating wrong self-diagnosis and self-treatment12.Our understanding of the etiology, microbiology, therapeutic response, and recurrence following treatment and phenotypic outcome associated with BV is hampered by difficulty in diagnosis. As a result, self-diagnosis and self-treatment by symptomatic women are regularly employed and encouraged. In the UK, if a woman has a vaginal discharge that is abnormal for her, she generally assumes this is vulvovaginal candidiasis (VVC) until proven otherwise, despite the fact that BV is the commonest cause of vaginal discharge in high-income countries, responsible for one-third of all vaginal infectious morbidity and twice as common as VVC. In a study in three general practices in the UK, 287 asymptomatic women who attended for routine cervical cytology testing volunteered to provide an extra vaginal swab for VVC and BV analysis. Despite the fact that these women were asymptomatic and had attended their general practitioner’s (GP’s) surgery for routine cervical cytology rather than because they were symptomatic, 9% had BV and 3.8% had VVC. While this was not surprising, a significant percentage (7.7%) of those with BV had taken antifungals in the previous month , by using Amsel composite clinical criteriaExisting diagnostic modalities are not well served by cultivation-dependent techniques and rely upon quantitative vaginal Gram stain microscopy that suffers from a degree of subjectivity. In the UK and continental Europe, in many laboratories, the microscopy is often performed by junior microbiology laboratory technicians who, following training and experience, are then promoted to more senior posts, and the responsibility for vaginal Gram stain diagnosis passes to new trainees. This is also probably why the PREMEVA Study provided negative results due to flaws in the diagnosis of BV. In their protocol, the investigators recognized that Nugent scoring was rare in France and medical laboratories would require training in the technique. To standardize the diagnosis of BV using Nugent scoring, technicians in 149 laboratories in 40 centers across France were given a training video, a technical brochure, and “training slides”, but no further details were provided. There was a significant delay of 5 years from presentation in abstract form in 2013 at the SMFM meeting to publication in 2018. In 2015, one of the PREMEVA co-authors admitted that the reason for the delay was because of concerns about the diagnosis of BV by Gram stain . Their intention was to repeat the diagnosis using molecular diagnostic methods , but in the published paper, this was not the case. Accordingly, this emphasizes that there is a need for a more robust and objective diagnostic test for vaginal eubiosis and dysbiosis.31. Together with a number of subsequent studies that have been supportive of the developments over traditional methods of diagnosing BV36, advances have been made, albeit they may be complementary to, rather than replace, culture-based techniques37. These can be summarized as follows. Using species-specific primers in 27 women with BV, and 46 without BV, a total of eight different bacteria were significantly associated with BV (P <0.001). These were BVAB-1 (now proposed to be renamed Candidatus Lachnocurva vaginae)5, BVAB-2, BVAB-3 (now renamed M. indolicus)6, Gardnerella, Leptotrichia, Megasphaera, and Atopobium species, and Eggerthella-like uncultured bacterium. With a few exceptions, the combination of bacterium-specific polymerase chain reaction (PCR) assays did not substantially improve sensitivity or specificity38. In a follow up study that addressed the diagnostic accuracy of these target organisms tested against Amsel criteria and Nugent score, PCR detection of one or more of these fastidious organisms was a more reliable indicator of BV than the presence of G. vaginalis alone  also produces ammonia, and this acts as a substrate to promote the growth of G. vaginalis43. Accordingly, since A. vaginae and G. vaginalis are frequently detected in association with BV, a number of authors examined the possibility of combining these two organisms as a means of diagnosing BV46. Using species-specific PCR for G. vaginalis and A. vaginae after obtaining 145 vaginal samples from healthy pregnant and non-pregnant women, researchers found that the comparative ROC analysis was poor, with an accuracy of 63–68%. In contrast, detection of the simultaneous presence of A. vaginae and G. vaginalis to diagnose BV had an accuracy of 87.8% (P <0.001), a sensitivity of 78%, and a specificity of 98%46. In another study, A. vaginae and G. vaginalis were highly sensitive for BV , but A. vaginae was more specific than G. vaginalis . A. vaginae was rarely detected without G. vaginalis, and in women in whom both organisms were detected, there were higher rates of recurrence (83%) than in women infected with G. vaginalis alone47. Ninety-six clinical samples sent for BV diagnosis were tested for A. vaginae, G. vaginalis, M. mulieris, and Bacteroides fragilis using real-time PCR. Twenty-eight samples (29%) contained A. vaginae, 26 of which (93%) also contained G. vaginalis. In the 68 samples without A. vaginae, only 7 (10%) contained G. vaginalis45. Using a quantitative threshold, 19/20 BV samples had a DNA level for either A. vaginae of ≥108 copies/mL or G. vaginalis of ≥109 copies/mL and 9/20 had both. The combination of an A. vaginae DNA level of ≥108 copies/mL and a G. vaginalis DNA level of ≥109 copies/mL demonstrated the best predictive criteria for the diagnosis of BV with excellent sensitivity (95%), specificity (99%), NPV (99%), and PPV (95%)40.31, or nucleic acid amplification test (NAAT) assays. The commonest target for the identification of bacteria using molecular-based techniques is the small ribosomal subunit of the 16S rRNA gene, which is useful because it is present in all bacteria and has regions of conserved sequence that can be targeted by universal (broad-range), or specific primers. It also has areas of heterogeneity that can be used to identify bacteria or to infer phylogenetic relationships through comparisons with known organisms in sequence databases7. The 16S rRNA gene has a length of approximately 1,540 nucleotides and contains nine hypervariable regions: V1 to V9. These hypervariable regions exhibit variable degrees of sequence diversity among different bacterial genera. The V1–V2, V3–V4, or V4 regions are most often targeted in microbiota studies. Universal PCR primer sets are designed to amplify as many different 16S rRNA gene sequences from a wide range of bacterial species as possible. However, there are no suitable 100% conserved regions of the 16S rRNA gene available for PCR amplification48.New technologies employing molecular markers for BV have been developed to overcome the problems associated with the Amsel criteria, vaginal Gram stain microscopy, and POCTs. Molecular tests have the advantage of i) objectivity, ii) quantification, iii) detection of fastidious organisms, and iv) validity for self-obtained vaginal swabs. The techniques are dependent upon the detection of specific bacterial nucleic acids and are primarily available as direct probe assays, as already mentioned50, two of which are FDA cleared51: i) NuSwab 49, ii) SureSwab , iii) BD MAX™ Vaginal Panel (VP) 51, iv) MDL BV Panel , and v) Hologic Aptima® BV Assay 50. These are NAATs that are capable of detecting as little as one organism per sample. In a short period of time, specific nucleic acid sequences are enzymatically amplified exponentially to produce billions of copies of the sequences. The amplified products are detected and identified. Such real-time PCRs eliminate the need for post-amplification analysis and reduce the risks of contamination. The bacteria that are positive or negative predictors of BV and other microorganisms detected in the various tests and the test characteristics and comparator standards are listed in Currently in the USA, there are a number of tests for the molecular diagnosis of BV in symptomatic women, most if not all of which are multiplex PCR assays. While some of these laboratories have a small throughput, to our knowledge, there are five commercially available molecular diagnostic tests for the diagnosis of BV that dominate the market in the USAA. vaginae, BVAB-2, and Megasphaera spp., which are predictive of BV, and L. crispatus, which is a negative predictor of BV7. G. vaginalis was not included because G. vaginalis can be detected in up to 55% of women with no clinical signs of BV28. An extended version detects i) Candida albicans; ii) Candida glabrata; iii) C. trachomatis, iv) N. gonorrhea. and v) Trichomonas vaginalis (TV)52 (see https://files.labcorp.com/testmenu/180021.pdf).The NuSwab detects G. vaginalis, ii) A. vaginae, and iii) Megasphaera spp. as positive predictors of BV and iv) L. crispatus, v) L. acidophilus, and vi) L. jensenii as negative predictors of BV. Sensitivity, specificity, PPV, and NVP were not reported, as the complexity of a BV diagnosis is based upon certain indicator organisms and is an example of how the reported result is interpreted according to data from Quest Diagnostics 49 (see https://testdirectory.questdiagnostics.com/test/test-detail/17333/sureswab-vaginosisvaginitis-plus?cc=MASTER).The SureSwab detects i) G. vaginalis, A. vaginae, Megasphaera type-1, and BVAB-2 are used as positive predictors of BV and L. crispatus and L. jensenii are used as negative predictors of BV. The proprietary algorithm provides a laboratory report as positive or negative for BV. The test also detects i) TV, ii) Candida group , iii) C. glabrata, and iv) Candida krusei51.The BD MAX™ VP is the first FDA market-authorized, microbiome-based PCR assay that directly detects the three most common infectious causes of vaginitis: i) BV, ii) VVC, and iii) trichomoniasis . For the diagnosis of BV, 19 and was found to have excellent specificity (98%) and NPV (95%)55.The future also holds hope for new technologies such as machine-learning/artificial intelligence. In a recent study, a machine-learning-based algorithm of seven bacterial strains was measured against the Ison/Hay criteriaG. vaginalis, ii) A. vaginae, and iii) Megasphaera types 1 and 2. L. crispatus was left out of the final model because it did not contribute to diagnostic accuracy53 (see https://www.mdlab.com/forms/TechBulletin/BV_Panel_Lactobacillus.pdf).The MDL BV Panel targeted nine vaginal bacteria as potential markers for BV. Using quantitation and ROC curves, three organisms were chosen as predictors of BV: i) in vitro NAAT that uses real-time transcription-mediated amplification for the detection and quantitation of ribosomal RNA from bacteria associated with BV. Two organisms were chosen as positive predictors of BV  and three species of Lactobacilli  were chosen as negative predictors of BV. The assay reports a qualitative result for BV rather than results for individual organisms50 and is intended to aid in the diagnosis of BV on the automated Panther® system using clinician- and patient-collected vaginal swabs from women with signs and symptoms of vaginitis and/or vaginosis .The Hologic Aptima® BV assay is an 54. Two of these (BD MAX™ VP and Hologic Aptima® BV assays) have already been outlined above. The other is the AmpliSens® (sometimes referred to as the ATRiDA test) Florocenosis/Bacterial vaginosis-FRT PCR test . The AmpliSens® test measures the relative concentration of i) Lactobacillus spp., ii) G. vaginalis clades 1 and 2, iii) A. vaginae, and iv) total bacteria to diagnose BV.To our knowledge, currently in Europe, there are three commercially available molecular diagnostic tests for the diagnosis of BV, all of which are multiplex PCR assays that are CE-IVD marked, are fast, and have a high sensitivity and specificityG. vaginalis, A. vaginae, and Lactobacillus spp. in relation to overall bacterial load. The ATRiDA test outcome was compared to the clinical BV diagnosis and to vaginal microbiota composition, determined by 16S rRNA gene sequencing, which enables accurate characterization of complex microbial communities with respect to membership and their relative abundance to each other. Based on statistical analyses of vaginal microbiota data, BV has been defined as ≤47% relative abundance of Lactobacillus spp. and an increased presence of anaerobes. Although recommended by some, microbiota analysis is currently quite laborious and expensive for it to be used routinely in clinical practice57.The accuracy of a commercially available multiplex PCR (ATRiDA) for the diagnosis of BV was evaluated in women reporting urogenital symptoms and women notified for STIs who were not necessarily symptomatic. The ATRiDA test  targets 58.Overall, compared to the clinical BV diagnosis, the ATRiDA test demonstrated high sensitivity (96.9 %) and moderate specificity (70.2 %). The NPV was >97.3% and the PPV differed by study group but was highest in women reporting urogenital symptoms (78.2 %). 16S rRNA gene sequencing showed that 54 % of women who were ATRiDA BV positive but clinically asymptomatic had a diverse anaerobic vaginal microbiota, classified as asymptomatic vaginal dysbiosis. The authors concluded that the ATRiDA test was a sensitive method for the detection of BV but advised that a positive test should be interpreted in conjunction with clinical symptoms59, was used as a reference test. Based on the microbiota analysis, the sensitivity of detecting BV was 38.9% for culture, 61.15% for Amsel criteria, 63.9% for Nugent score and the BD MAX™ VP assay, and 80.6% for the AmpliSens® assay. The specificity of all methods was ≥92.4%. The authors concluded that, compared to Amsel criteria, Nugent score, culture, and the BD MAX™ VP assay, the AmpliSens® assay offered optimal agreement with microbiota analysis. However, concerns were expressed that the handling of samples was not performed in accordance with the manufacturer’s instructions for the BD MAX™ VP assay. The eSwabs were tested with the BD MAX™ VP assay by pipetting 500 μl of eSwab into 500 μl of BD MAX™ Sample Tube Buffer. Although this was done in consultation with BD, it might have influenced the sensitivity of the assay. Furthermore, the report did not describe the advantage of the BD MAX™ VP assay to test for BV, VVC, and TV as a single test. For the diagnosis of VVC, the performance of the BD MAX™ VP assay was comparable to other applied methods and detected additional positive samples compared to the current gold standard . None of the included samples was positive for TV by the applied assays, including the BD MAX™ VP assay. One of the classifications was for aerobic vaginitis (AV) as a separate identity from BV, the significance of which, particularly as a predictor of PTB, has been questioned60.Recently, two CE-IVD-marked real-time PCR assays (AmpliSens® and BD MAX™ VP) were compared with Amsel criteria, Nugent score, and/or culture for the diagnosis of BV. Microbiota analysis, based on amplicon sequencing of the 16S rRNA gene, described elsewhere19, the presence of clue cells, and a significant growth of G. vaginalis, ii) yeast culture, and iii) a combination of culture, wet mount microscopy, and an alternative molecular assay61. The sensitivity and specificity for BV was 89.8% and 96.5%, respectively. For TV, the sensitivity and specificity were both 100%, and for VVC, the sensitivity and specificity were 97.4% and 96.8%, respectively. The authors concluded that the BD MAX™ VP was highly sensitive and specific and simplified the identification of vaginitis of infectious origin.Another recent study evaluated the BD MAX™ VP for the diagnosis of BV, VVC, and TV. The test was compared with i) a combination of Ison/Hay criteria62. Using Nugent scoring as a reference, three molecular assays were assessed for the diagnosis of BV, examining the impact of incremental increases in bacterial targeting. The introduction of A. vaginae improved specificity for the diagnosis of BV, but co-infection with VVC was common (13.5%)63, and the shortcomings of molecular-based techniques in the presence of mixed vaginal infections have been emphasized64.Finally, in a group of 200 symptomatic women, the performance of the BD MAX™ VP was compared to that of the Affirm test, which, for the purpose of the study, was considered to be the standard of care. Both multiplex assays are commercially available for the detection of DNA from organisms associated with vaginitis, including BV, VVC, and TV. The sensitivity and specificity of the BD MAX™ VP for BV was 96.2% and 96.1%, respectively, compared to 96.2% and 81.6%, respectively, for the Affirm test. The sensitivity and specificity of the BD MAX™ VP for VVC was 98.4% and 95.4%, respectively, compared to 69.4% and 100%, respectively, for the Affirm test. The BD MAX™ VP and Affirm test demonstrated 100% concordance for the detection of TV. The authors concluded that the results demonstrated an improvement in accuracy of the BD MAX™ VP compared to the Affirm test for the detection of BV and VVC19 and compared with a molecular test analyzing six different bacteria associated with BV  in relation to Lactobacillus spp. using real-time PCR. There was excellent agreement between the compared methods, with a kappa coefficient value of 0.87 (0.76–0.99). Compared to the modified Ison/Hay criteria, the molecular test achieved a sensitivity of 91%, specificity of 97%, PPV of 91%, and NPV of 97%65.In a recent Swedish study of women undergoing termination of pregnancy, BV was determined to be present or absent on the basis of a modified Ison/Hay criteria assessment49.Microarray analysis and next-generation sequencing (NGS) have contributed to our understanding and development of commercially available tests for BV, but, as yet, these assays are generally performed in large reference laboratories and, outside North America, such as the UK and continental Europe, are not yet commercially available as a routine option66. The etiology of “the BV syndrome” remains unknown. BV is the most common cause of vaginal discharge globally, with an estimated annual economic burden of $4.8 billion68. BV has important public health implications such as PTB and increased risk of acquisition of STIs such as gonorrhea, chlamydia, TV, HSV, and HIV69. These are associated with serious mortality and morbidity for women and babies. The place of the condition as a syndrome, together with the proposed potential mechanisms, suggested etiological and predisposing factors, overlying sociodemographic variables, and the different phenotypic outcomes are presented in 70. At the center of the debate is whether or not BV is caused by a primary pathogen or a polymicrobial, synergistic consortium of microorganisms that are sexually transmitted71.Women with symptoms of vaginal dysbiosis may present to their GP, a genitourinary medicine (GUM) clinic, a family planning clinic, or directly to a gynecologist, among whom the services offered and the management and diagnostic skills such as microscopy may differ72, which was recently updated, suggested that BV originates through the sexual transmission of G. vaginalis73. Using its virulence factors, it successfully colonizes the host’s vaginal epithelium and forms a biofilm community to overcome Lactobacilli. G. vaginalis’s genetic diversity may lead to both pathogenic and non-pathogenic strains. Symbiotic relationships with normally dormant vaginal anaerobes lead to increases in the latter, which contribute to the symptoms of BV. Critics of this model point out that G. vaginalis can be found in a virgo intacta and in sexually active women with a healthy vaginal microbiota and that colonization with G. vaginalis does not always lead to BV74, though this has been challenged75. Nevertheless, recent research into the pathogenesis of BV has determined the existence of a number of different species within the Gardnerella genus. It may be that healthy women are colonized by non-pathogenic species of Gardnerella, whereas virulent strains are involved in the development of BV72.A conceptual modelC. trachomatis, N. gonorrhoeae, and TV DNA were calculated, and concordance rates between the assays for the detection of TV were determined. Women with BV alone or with concurrent VVC had high rates of co-infection with STIs (24.4–25.7%). TV results were concordant between the two assays in 559 of 560 samples tested. These data suggest that women with symptoms of vaginal dysbiosis in the form of BV or candidiasis may be at risk of an STI. Accordingly, molecular testing may provide a broader diagnostic coverage for symptomatic women regardless of the type of clinic in which they are seen76.To address this problem, the likelihood of STIs was assessed in symptomatic women tested for vaginal dysbiosis using both the BD MAX™ VP and the BD MAX™ CT/GC/TV molecular diagnostic assays. Positivity rates for 77. Since such tests are simple, convenient, reliable, and inexpensive, perhaps every woman suspected of having BV should have a simultaneous molecular test (PCR or gene sequencing) for other STIs, albeit this may depend on the patient’s risk factors.Symptomatic women attending a GUM clinic in the UK had two vulvovaginal swabs taken. One was used for a chlamydia and gonorrhea NAAT and one for testing on the BD MAX™ VP. Speculum examination was then performed and vaginal swabs were taken for vaginal pH and examination by wet-mount and Gram stain microscopy for VVC, BV, and TV. Forty-six (23.6%) women were negative for all three infections on the BD MAX™ VP. Ninety-three were positive for BV (47.7%), 70 (35.9%) for VVC, and 9 (4.6%) for TV. Thirty-six women tested positive for both BV and VVC on the BD MAX™ VP. The test sensitivity for VVC was 86.4% and the specificity was 86.0%. For BV, the sensitivity was 94.4% and the specificity was 79%. While the sensitivity for BV was encouraging, the specificity was lower than expected. This may reflect the higher rate of STIs in the study population, which worsened the vaginal dysbiosis. The author concluded that while NAATs do not provide immediate results, they were easy to process and offered advantages over a high vaginal swab performed in other settings where rapid microscopic assessment was not readily available49. A recently updated ACOG practice bulletin on vaginitis was released78 that applied to BV, VVC, and TV. This still recommends traditional methods for diagnosing vaginitis such as culture, Amsel criteria, and Nugent score. While the bulletin acknowledges that PCR diagnostic tests exist and can be considered for use, it states that they have “comparable sensitivity and specificity to Nugent scoring and Amsel criteria”, citing three studies79. However, to justify this statement, at least one of the studies cited actually showed an improvement in performance of NAAT compared to Amsel criteria and clinician diagnosis79. Furthermore, a recent study using a different NAAT to test for BV demonstrated a similar improvement compared to Amsel criteria and clinician diagnosis45. Many researchers agree that molecular assays are superior to conventional methods such as Amsel, Gram stain, and DNA probes like the Affirm test. What is difficult to explain is why they are not in more widespread use in clinical practice. This is almost certainly a problem in educating the general public, primary care physicians, and specialist clinicians of the potential benefits of these technologies. In the UK, our experience is of academic centers testing and validating the technology, following which they are keen to introduce the technology, but a business case has to be made, which takes time with budgets planned a year in advance. In addition, the COVID-19 pandemic has disrupted research, clinical care, and funding in other areas.Currently, there are a number of diagnostic tests available to diagnose BV, ranging from POCTs to molecular assays. The choice of test will be influenced by the cost, availability, speed, and accuracy of the test. There are few recommendations with respect to the use of molecular-based technology to screen for BV, especially in asymptomatic womenIn the USA, it is our understanding that the acknowledgement of the benefits of NAATs suffers from the dynamics between regulatory guidelines and payer/reimbursement. While the ACOG guidelines acknowledge the role of NAATs, they do not provide a firm endorsement. Since the funders of healthcare rely heavily on such guidelines, their policies do not support full reimbursement of NAAT for BV. Simultaneous NAAT for STIs is likely to provide further resistance. It is likely to require stronger endorsement in the guidelines of the ACOG or the CDC for NAATs to diagnose BV, TV, VVC, and other STIs to be funded.G. vaginalis in favor of multiplex PCR technology that is able to detect multiple indicator (or contra-indicatory) organisms.This review is designed to increase knowledge about the different molecular assays available, the range of targeted microorganisms, and the test characteristics with respect to sensitivity, specificity, PPV, and NPV. Nevertheless, since BV is a polymicrobial condition, diagnosed (whether by molecular or microscopy techniques) by the presence of potentially dysbiotic organisms and the absence of eubiotic organisms, investigators should avoid the use of direct probe assays of single BV indicator organisms such as 14 , unless they consider that reports such as “unspecified dysbiosis” or “equivocal” or “unclassified” are synonymous for Nugent’s intermediate category. It is important to recognize that this intermediate category is a Gram-stain diagnosis, rather than one that is made clinically, and that this category is a distinct entity not only because amalgamation with grade III  diminishes the specificity and PPV of the Gram-stain for diagnosing BV but also because women with grade II Nugent score often fail to respond to clindamycin, whereas those of grade III do not. Although a distinct entity, the intermediate category is likely to be a transient state on a continuum from a normal microbiota to BV and vice versa81. Our recommendation would be that if the result of the test returns as i) unspecified dysbiosis, ii) equivocal, iii) unclassified, iv) intermediate, or v) indeterminate, repeat the test after a short interval of 10–14 days, and if the result is still unclear and the patient is symptomatic, they should be treated.None of the molecular-based tests discussed address the “intermediate” category on Nugent’s score9, and L. iners may play an important part in the etiology of PTB83. None of the molecular diagnostic tests discussed above targeted L. iners. This is understandable, since the presence or absence of L. iners does not differentiate between vaginal eubiosis and dysbiosis. While L. iners is commonly found in BV as well as in the eubiotic vagina, it is rare to find one of the other “big three”  in abundant numbers in the presence of BV and, conversely, it is rare to find BV when these three species of Lactobacilli are found in abundance in the presence of minimal diversity of other potentially pathogenic organisms. Nevertheless, the targeting of L. iners might improve sensitivity and specificity if the relative abundance of L. iners and the diversity of other organisms were known and taken into consideration. In a study of the vaginal microbiome in women with PPROM84, using relative abundance and diversity, it was possible to differentiate between the presence of L. iners in a eubiotic as well as a dysbiotic vaginal microbiota. In a eubiotic vaginal microbiota, the diversity of organisms is as low as two or three organisms, whereas in BV, the diversity is significantly higher (eight or more)7. In a vaginal microbiota that was dominated by an abundance of L. iners, with or without L. crispatus, L. gasseri, or L. jensenii, where there was minimal diversity of other organisms in the microbiota, this could be classified as eubiotic. In contrast, in a vaginal microbiota that was dominated by an abundance of L. iners, in the absence of any other species of Lactobacilli, and a high level of diversity , this was clearly dysbiotic (unpublished data).There is increasing interest in the vaginal microbiome as a biomarker for PTBG. vaginalis, compared to other BV-associated bacteria, to form a biofilm that provides a scaffold to which other potentially pathogenic bacteria can attach88 may be very important. While the presence or absence of G. vaginalis has importance, quantitative analysis is more important with both traditional cultivation-independent89 as well as novel molecular-based techniques56. These demonstrate that a heavy growth, rather than simply presence or absence, is more predictive of pathology that leads to adverse sequelae89 and emphasizes the importance of quantitative PCR analysis .The propensity of G. vaginalis can be identified as a number of biotypes that render the species phenotypically and genetically heterogeneous. Phenotypic diversity within the species is manifest by i) virulence factors, ii) cytotoxicity, iii) production of lytic enzymes such as vaginolysin and sialidase that break down the local secretory IgA host response, iv) biofilm formation, v) susceptibility to antibiotics, and vi) adhesion and ability to dislodge species of Lactobacilli from vaginal epithelial cells56. Genomic analysis of species of G. vaginalis has identified four clades (1–4). A clade is defined as “a group of organisms believed to comprise all the evolutionary descendants of a common ancestor”. An example is the Hominids, a clade occupied by humans, chimpanzees, gorillas, orangutans, and gibbons. The four clades of G. vaginalis are different with respect to genome size and G:C (guanine:cytosine) ratio, sufficient for them to be considered as separate species. In a study that aimed to evaluate the distribution and abundance of the G. vaginalis clades and sialidase production, the quantification of all four G. vaginalis clades discriminated between a BV microbiota and a normal microbiota more accurately than measuring G. vaginalis sialidase production. Clade 4 was strongly associated with a BV microbiota, despite most clade 4 strains lacking the sialidase A gene. These genetic differences among strains might identify potentially pathogenic from commensal strains of G. vaginalis93 and the host’s response to each95. For further information, the reader is guided to an excellent review4. These factors may be important in the choice of clades of G. vaginalis targeted in molecular-based tests for BV and vaginal dysbiosis58.Based on enzyme activity and biochemical usage of various sugars, G. vaginalis may mirror the confusion over the role of the genital mycoplasmas in adverse outcomes of pregnancy96. Genital species of Ureaplasma are detected frequently in healthy, asymptomatic individuals. Using cultivation-dependent techniques in women delivering between 26 and 34 completed weeks of gestation, 80% and 24% of those in spontaneous labor were colonized by Ureaplasma urealyticum and M. hominis, respectively. In contrast, in women not in labor but delivered electively for fetomaternal indications at the same gestational age, 46% and 8%, respectively, were colonized by U. urealyticum and M. hominis97. This qualitative analysis was significantly different for U. urealyticum (P <0.01) but not for M. hominis. Nevertheless, when only a heavy colonization of M. hominis was considered (>105 color change units [the unit of quantity at the time]), 13 women in the study group (18%) and none of the controls were identified (P <0.05), suggesting that it was not the presence or absence of the organism that was important but the quantity present89. Only certain subtypes of the species are pathogenic. Using molecular-based techniques, it was found that Ureaplasma species consist of 14 serovars from two biovars. The majority of human Ureaplasma isolates belong to Ureaplasma parvum (biovar 1) comprising four serovars , with U. urealyticum (biovar 2) comprising 10 serovars isolated much less often. However, the data are limited and conflicted because of the difficulties with traditional genotyping methods98. This may be relevant to the confusion about the role of U. urealyticum in adverse outcomes of pregnancy96.The phenotypic and genotypic variation among strains of 99. Mycoplasma genitalium is an STI that shares similar clinical aspects with C. trachomatis and should be treated if detected. Nevertheless, further research is necessary before M. genitalium is included in molecular-based tests for BV.The genital mycoplasmas have been implicated in a number of adverse outcomes in obstetrics and gynecology, and their role is complicated by the presence or absence of BVL. crispatus as much as the presence of positive markers such as the combination of G. vaginalis and A. vaginae. They should also take into consideration quantitative as well as qualitative analysis. While they cannot account for rarely detected organisms whose role in the BV syndrome remains unknown, they must continue to study these organisms and make clinicians aware of the false negatives and shortcomings of the test because these organisms may not have been incorporated. It is important to recognize and investigate further the role of L. iners in vaginal eubiosis and dysbiosis. Presence or absence is not as important as relative abundance and diversity among other organisms present when differentiating between eubiosis and dysbiosis in the case of L. iners. Future tests should recognize and incorporate this anomaly. The choice of primers used for studying the vaginal microbiota has important implications on the evaluation of NGS of the vaginal microbiota using partial 16S rRNA gene amplicons and has been discussed elsewhere9. Primers spanning the V3–V4 region identify more taxa in the vaginal microbiota than those from the V1–V2 region, particularly taxa such as G. vaginalis. This is also important when choosing which clade of G. vaginalis or biovar of Ureaplasma is being targeted. The future also holds hope for new technologies such as machine-learning and artificial intelligence.New information from molecular-based cultivation-independent techniques has identified candidate organisms for inclusion in multiplex diagnostic tests. This review presents separate data from unrelated studies, not comparative differences from a single study or a systematic review or meta-analysis. Such diagnostic techniques demonstrate high sensitivities, specificities, PPVs, and NPVs against existing gold-standard diagnostic techniques and will hopefully continue to improve, as our understanding of the vaginal microbiome expands. However, further development and refinement are necessary, and care must be taken when measuring these parameters against different BV diagnostic standards in different molecular diagnostic tests. Testing needs to reflect the importance of the absence of negative markers of BV such as in vitro diagnostic; CST, community state type; FDA, Food and Drug Administration; GAIN, Generating Antibiotic Innovation Now; GP, general practitioner; GUM, genitourinary medicine; HIV, human immunodeficiency virus; HSV, herpes simplex virus; NAAT, nucleic acid amplification test; NGS, next-generation sequencing; NPV, negative predictive value; PCR, polymerase chain reaction; POCT, point of contact test; PPROM, preterm prelabor rupture of the membranes; PPV, positive predictive value; PTB, preterm birth; ROC, receiver operating characteristic; STI, sexually transmitted infection; TV, Trichomonas vaginalis; VP, vaginal panel; VVC, vulvovaginal candidiasis.BV, bacterial vaginosis; BVAB, bacterial vaginosis-related bacterium; CE-IVD, Conformité Européenne


Malassezia yeasts are lipid dependent and part of the human and animal skin microbiome. However, they are also associated with a variety of dermatological conditions and even cause systemic infections. How these yeasts can live as commensals on the skin and switch to a pathogenic stage has long been a matter of debate. Lipids are important cellular molecules, and understanding the lipid metabolism and composition of Malassezia species is crucial to comprehending their biology and host–microbe interaction. Here, we investigated the lipid composition of Malassezia strains grown to the stationary phase in a complex Dixon medium broth. In this study, we perform a lipidomic analysis of a subset of species; in addition, we conducted a gene prediction analysis for the detection of lipid metabolic proteins. We identified 18 lipid classes and 428 lipidic compounds. The most commonly found lipids were triglycerides (TAG), sterol (CH), diglycerides (DG), fatty acids (FAs), phosphatidylcholine (PC), phosphatidylethanolamine (PE), ceramides, cholesteryl ester (CE), sphingomyelin (SM), acylcarnitine, and lysophospholipids. Particularly, we found a low content of CEs in Malassezia furfur, atypical M. furfur, and Malassezia pachydermatis and undetectable traces of these components in Malassezia globosa, Malassezia restricta, and Malassezia sympodialis. Remarkably, uncommon lipids in yeast, like diacylglyceryltrimethylhomoserine and FA esters of hydroxyl FAs, were found in a variable concentration in these Malassezia species. The latter are bioactive lipids recently reported to have antidiabetic and anti-inflammatory properties. The results obtained can be used to discriminate different Malassezia species and offer a new overview of the lipid composition of these yeasts. We could confirm the presence and the absence of certain lipid-biosynthesis genes in specific species. Further analyses are necessary to continue disclosing the complex lipidome of Malassezia species and the impact of the lipid metabolism in connection with the host interaction. Malassezia species belong to the phylum Basidiomycota and are the most important constituent of the human skin mycobiota. Malassezia species have been associated with dermatological conditions, such as dandruff/seborrheic dermatitis, pityriasis versicolor, and atopic dermatitis, and with more severe conditions, like systemic infections and pancreatic cancer  in Malassezia species is determined by the absence of genes that encode for FA synthase in their genomes  has been developed, and its application —and a previously reported isolate of M. furfur with atypical assimilation of Tween 80   at 40% amplitude, performing the following procedure 10 times: 1 min sonication and 30 s of cooling on ice. The disrupted cells were centrifuged at 4°C at 5,000 g for 10 min, and the pellet was washed with deionized water and centrifuged using the same conditions. Each sample was freeze-dried and weighted before analysis.Aliquots of R = 10,000 for positively charged lipids and with an Agilent 6550 QTOF mass spectrometer with resolution R = 20,000 for negatively charged lipids. The mobile phase A was a 90:10 mixture of isopropyl alcohol and acetonitrile with 10-mM ammonium formate and 0.1% formic acid. The mobile phase B was a 60:40 mixture of acetonitrile and water with 10 mM of ammonium formate and 0.1% formic acid. The elution gradient was 0 min 15% (A), 0–2 min 30% (A), 2–2.5 min 48% (A), 2.5–11 min 82% (A), 11–11.5 min 99% (A), 11.5–12 min 99% (A), 12–12.1 min 15% (A), and 12.1–15 min 15% (A). Raw data were processed qualitatively by Agilent's MassHunter software. Peak alignment was performed using MassProfiler Professional. MS/MS information and the LipidBlast library were used to identify the lipid compounds. A unique ID was given to each lipid, based on its retention time and mass–charge ratio, and an additional manual verification was made. Lipids were identified based on MS/MS fragmentation patterns using in-house Lipidblast software . Lipid extraction was conducted according to the center's workflow involving sample extraction based on the “Matyash” method  the whole chromatographic profiles and (b) the sole basis of the FAHFA lipids, we built partial least squares models coupled with discriminant analyses (PLS-DA). Briefly, the relative amount of lipid compounds was used as a predictor of a categorical variable, the strain. The use of DA alone would lead to spurious predictive models for two main reasons. First, groups of lipid compounds tend to co-occur throughout the analyzed samples, which leads to significant correlation between them and, thereby, to multicollinearity, an undesirable property for a set of predictors. Second, the number of predictors was very large (above 400 compounds for the whole lipidome and 77 for the FAHFA lipids) and far larger than the number of analyzed samples . The PLS model reduces the number of predictors to a lower number of uncorrelated  variables, which are then used to discriminate (DA) among categories (strains). PLS is related to principal component analysis (PCA). Whereas, PCA reduces the dimensionality of predictors based on the sole covariation between them, PLS further considers the ability of the new components to predict the output variables.To test whether To estimate the lowest number of components that suffice to discriminate among strains, we first fitted complete PLS-DA models and then assessed the classification performance of our models. We used unscaled maximal multivariate distances because all descriptors (compound concentrations) were expressed in the same units. The performance curve was used to visualize the contribution of each additional principal component (PC) to reduce the error rate of classifications to the point that adding new components represented negligible  contributions. As expected, both overall error rate and balanced error rate in classification decreased with the number of components and then stabilized after the fifth  or eighth (FAHFA lipids) PC. Therefore, we used five and eight PCs, respectively, in the following analyses.2, Q2, and DQ2 statistics . If this stage yielded less than four sequences, the possible hits were expanded to those coming from all fungi (taxID:4751). If this approach yielded <10 sequences, then all NCBI databases were considered, and duplicates were removed manually.A custom BLAST protein database with the downloaded sequences was created using BLAST V.2.6.0+  It had a BLAST hit against one of the sequences associated with the corresponding protein, with an average nucleotide identity score of 60% or higher and an e-value of 1e-5 or less. (2) It was grouped by OrthoFinder in an orthologous cluster associated with the same protein. Here, an orthologous cluster was considered to be associated with a given protein if at least 60% of the downloaded sequences from that protein were present and if they constituted at least 40% of the total number of sequences in the cluster. Note that the orthologous clusters were created de novo with OrthoFinder using the lipid metabolism and Malassezia sequences. (3) It had a HMM that can hit against the HMM associated with that protein, with a coverage of at least 60% of the protein and an e-value of 1e-5 or less. All Malassezia sequences that fulfilled these criteria were subjected to a final verification in which their domains and motifs were identified with InterProScan   and cardiolipin were also detected but in a low concentration (data not shown). We also detected lysophosphatidylcholine, lysophosphatidylethanolamine, and lysophosphatidylinositol , of which we detected 77 different molecular species . Our ana strains . In M. f species .Pityrosporum ovale and 9-hydroxystearic to a lower extent  . We coulMalassezia species.We did not perform a lipidomic analysis of mDixon broth only to address the possibility that some of the lipids in the medium associated to cells. However, the lipidome profiles showed clear differences among the species analyzed, indicating that similar amounts of lipids are not simply carried over to all strains. Further analysis is necessary to rule out specific association of certain lipid species in this media to the Malassezia species examined in this study using the lipid profiles in which we identified 428 molecular species  (p = 0.0013) when classifying each species against all others. The hierarchical clustering resulting from the sPLS-DA  . Hence,  sPLS-DA  showed t. furfur . The clu. furfur .Malassezia species. Four out of the six Malassezia species revealed positive associations with a unique compound. The other two were both associated with four TAG  compounds, but the atypical M. furfur was strongly and uniquely associated with TAG17 and TAG95, whereas M. pachydermatis was linked with a large number of TAG, FA, PE, and DG compounds , there were seven misclassifications (NMC = 7) in the other Malassezia species (p = 0.0013) when classifying the three former species but between 0.98 and 0.99 for the other ones. Interestingly, M. globosa revealed an idiosyncratic lipid profile of about 35 FAHFA compounds that were not associated with all other strains , related to cholesterol synthesis, was present. ERG8 , ERG27 (sterol C-3 dehydrogenase), ERG2 (sterol C-8 isomerases), and ERG3 (sterol C-5 desaturases) are apparently missing in M. sympodialis. ERG26 (sterol C-4 decarboxylases) homolog was present only in M. furfur and atypical M. furfur  were present in all the strains, but interestingly, these were absent in M. globosa. These transferases are predicted to catalyze the acylation of ergosterol, and the absence of CE in the lipidome of M. globosa , APP1, and LPP1 were present in M. furfur , a kinase related to the regulation of levels of PA, seems to be missing in all strains , is part of a sterol acetylation/deacetylation cycle.TAG and CE synthesis genes were detected in the genome of all the strains . DGA1 an globosa  can be e strains . In contMalassezia species. In M. sympodialis, neither PIS1 (phosphatidylinositol synthase), involved in the formation of PI  or OPI3 (CDP-diacylglycerol-serine O-phosphatidyltransferase), which catalyze the reactions to the formation of PC, were found. This latter gene was also missing in M. globosa. PLC1 encoding phospholipase C was missing in M. sympodialis. Homologs for enzymes related to the synthesis of PE via the Kennedy pathway, such as EKI1 (ethanolamine kinase), were apparently absent in all strains. The choline kinase gene CKI1, required for PC synthesis, was present only in M. pachydermatis  involved in the dephosphorylation of PGP to PG was apparently not present in M. pachydermatis , which was present only in M. furfur and atypical M. furfur; however, a paralog LCB3 was present in all strains  and DCI1 -delta -dienoyl-CoA isomerase) were detected in our analysis. FAD2/3 Δ12-desaturase/ω3-desaturase, involved in the synthesis of PUFAs such as linoleic acid  and α-linolenic acid , was present . The data reveal novel insights and are of importance to understand lipid metabolism in this yeast.Malassezia strains because the phylogenetic relations were maintained among the species during the examination of their lipid composition. In this study, we determined that the most common lipids of these strains are TAG, sterols, diglycerides, FAs, phosphatidylcholine, phosphatidylethanolamine, ceramides, sphingomyelin, acylcarnitine, and lysophospholipids. The lipid composition was similar to that of S. cerevisiae  that is related to cholesterol synthesis was found in the Malassezia genomes. Pneumocystis jirovecii, an important human pathogen, is characterized by the presence of cholesterol, and previous studies have suggested that it contributes to the flexibility of the membrane in the trophic form of these fungi , a conserved enzyme in eukaryotes, and the acetylation/deacetylation cycle performed by EKI1 was absent in all strains, and CKI1 was present only in M. pachydermatis). This probably means that Malassezia strains can synthesize PE and PC only using CDP-DAG and that M. pachydermatis can synthesize PC also via the Kennedy pathway. PLC1 encodes phospholipase C, forming DG, inositol, and G3P (glycerol-3-phosphate), which can serve again as precursors for phospholipid synthesis , and cardiolipin. CDP-DAG and Kennedy pathways can synthesize PE and PC   is an analog of phosphatidylcholine (PtdCho), which is synthesized by many soil bacteria, green plants, chromophytes, fungi, and amoebae , and Tween 40 (>90% pure). Ox bile is a complex lipid mixture containing bile salts and bilirubin cholesterol, FAs, and lecithins, which are a mixture of different phospholipids, glycolipids, or TAG (Hall and Guyton, The lipidomic analysis of the Malassezia species (Malassezia species have a preference for accumulating certain lipid sources from the medium. A lipidomic analysis of the medium is required to confirm that preferential association does occur. To eliminate the problem of carryover of lipids, synthetic media with pure lipids as a source should be used, but such a medium has not yet been developed. Furthermore, more work is required to link the current lipid analysis to lipidomic analysis of Malassezia species grown with culturing methods that resemble the in vivo conditions, which remains a major challenge. In addition, the role of gene products and their importance in the biosynthetic pathways require further studies, such as knockouts, mutant genes as well as studies with stable isotope labeled FAs coupled with lipidomic analysis to unravel these routes.There is a possibility that some of the lipidic components present in the mDixon associate with yeast cells and were detected in our analysis. The relative amount of the different lipid species we detected in the six different  species  do, howeMalassezia strains. This study contributes to the knowledge in this genus and provides fundamental information with which future studies can advance comprehensive knowledge of the role of lipids in the life cycle of Malassezia yeast as commensal and pathogen.Taken together, our data provide a general overview of the lipid composition and metabolism in six All datasets generated for this study are included in the article/AC, AA, AB, and SR contributed to the design of the work. AC performed the experiments. AC, AA, JC, LM-C, JA-M, and HC wrote the manuscript. AC, HC, SR, and AB made revisions. All authors were involved in the analysis and interpretation of data. All authors approved the version to be published and agreed to be accountable for all aspects of the work.The 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.


Coronavirus disease 2019 (COVID-19) is sweeping the globe and has resulted in infections in millions of people. Patients with COVID-19 face a high fatality risk once symptoms worsen; therefore, early identification of severely ill patients can enable early intervention, prevent disease progression, and help reduce mortality. This study aims to develop an artificial intelligence-assisted tool using computed tomography (CT) imaging to predict disease severity and further estimate the risk of developing severe disease in patients suffering from COVID-19.Initial CT images of 408 confirmed COVID-19 patients were retrospectively collected between January 1, 2020 and March 18, 2020 from hospitals in Honghu and Nanchang. The data of 303 patients in the People’s Hospital of Honghu were assigned as the training data, and those of 105 patients in The First Affiliated Hospital of Nanchang University were assigned as the test dataset. A deep learning based-model using multiple instance learning and residual convolutional neural network (ResNet34) was developed and validated. The discrimination ability and prediction accuracy of the model were evaluated using the receiver operating characteristic curve and confusion matrix, respectively.The deep learning-based model had an area under the curve (AUC) of 0.987  and an accuracy of 97.4% in the training set, whereas it had an AUC of 0.892 (0.828–0.955) and an accuracy of 81.9% in the test set. In the subgroup analysis of patients who had non-severe COVID-19 on admission, the model achieved AUCs of 0.955 (0.884–1.00) and 0.923 (0.864–0.983) and accuracies of 97.0 and 81.6% in the Honghu and Nanchang subgroups, respectively.Our deep learning-based model can accurately predict disease severity as well as disease progression in COVID-19 patients using CT imaging, offering promise for guiding clinical treatment. The coronavirus disease (COVID-19) is rapidly spreading worldwide, leading to a global crisis. The first outbreak was reported in Wuhan, China, in December 2019 , and theIn the field of artificial intelligence and medical big data analysis, using machine learning-based classification algorithms to automatically diagnose and judge the severity of disease and aid clinical decisions has always been important. Notably, deep learning, a complex machine learning approach that has emerged in recent years, performs much better on language and image recognition than previous related technologies . It has Currently, lesion annotation is necessary for most deep learning-based methods of disease diagnosis, particularly for disease detection in CT volumes. Considering the rapidly growing epidemic, significant effort is needed in terms of annotation by radiologists. Unfortunately, the current shortage of radiologists due to the urgent COVID-19 setting implies that annotation dependent algorithms may be insufficient for the present medical need. Thus, performing COVID-19 detection in an “unsupervised” manner while simultaneously maintaining good predictive accuracies is of huge importance. In this study, we established and verified a multiple instance deep learning model using CT imaging to predict disease severity and the risk of future development of severe COVID-19. This model is expected to assist doctors in performing clinical diagnosis and treatment.The Medical Ethics committee of Nanfang Hospital of Southern Medical University approved this retrospective analysis. Written informed consent from all study participants was obtained. This study included patients from the People’s Hospital of Honghu and The First Affiliated Hospital of Nanchang University, who met the following selection criteria: (1) confirmed case of COVID-19 with positive tests for 2019-nCoV nucleic acid and compliance with the guideline of Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia  developeThe disease severity spectrum of COVID-19 covers states from mild to critical, according to the guidelines for novel coronavirus pneumonia . In thisPatients who maintained mild or moderate symptoms were assigned to the non-severe group. Those with critical or severe disease conditions on admission and those who had moderate or mild disease on admission but developed severe COVID-19 after admission were assigned to the severe group.The event of severe illness-free survival was defined as disease progression and the censored subject was defined as no occurrence of endpoint event by the time of discharge. Severe illness-free survival was defined as the time from the diagnosis of COVID-19 to the occurrence of disease progression.All CT images were downloaded via a picture archiving and communication system. Considering that each patient had received several CT examinations during hospitalization, there were multiple slices for each series. The patterns of rows/columns, pixel spacing, slice count, and thickness distributions for the data from The People’s Hospital of Honghu and The First Affiliated Hospital of Nanchang University are shown in The overall preprocessing procedure for raw CT data was as follows. The window width and level were narrowed to 1600 and 600, respectively; a pixel value with <5 percentile was set to the value of 5 percentile; that with <95 percentile was set to the 95 percentile. All DICOM CT series were resampled to the target pixel , yielding median values in all CT series. In the resampled CT volume, the patch was cropped to as large as 3 × 224 × 224 (z × y × x); then, each CT value for the patch was scaled to the section between , and each patch CT value was normalized to a mean of 0.5 and a variance of 1 .https://download.pytorch.org/models/resnet34-333f7ec4.pth. The next steps included performing a full inference pass through the patch dataset, ranking the patches according to their probability of being positive or negative, and training the ResNet34 on the top four ranking patches from every three adjacent resampled CT slides. These three steps were repeated for 100 iterations, and a patient-level CT classification was generated via max-pooling of all the patches belonging to the same one  is a representation of deep convolutional neural networks integrated with images, auto-encoding, and classification. In a prostate cancer pathology dataset using more than 10,000 slides, ResNet34 was found to perform better than other architectures as a backbone model for multiple instance learning (MIL) . The arcsame one .Five-fold cross-validation was performed with 17,336 CT images from the resampled CT data of 303 patients who had been treated at the People’s Hospital of Honghu, and 6,476 CT images from the resampled CT data of 105 patients at The First Affiliated Hospital of Nanchang University were used as the test dataset.There are several varieties of the network architecture for the ResNet series, including ResNet34 , AlexNetSeven general classification metrics, including sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), false positive rate (FPV), false negative rate (FNR), accuracy, and AUC, were used to estimate the classifiers’ performance of all deep neural network models used for the training. Their methods of calculation are given in Equations (1) – (7).Here, TP is the true positive, TN is the true negative, FP is the false positive, and FN is the false negative; the AUC value was obtained using R statistical software.Our implementation was based on the PyTorch (version 1.0) pretrained model for the ResNet34 network. Our training experiments were conducted in a Linux environment on a machine for data loading, building models, and training .https://github.com/dchealth/covid-mil.git.The source code and our best weights of the trained ResNet34 for this work can be downloaded from: Continuous variable data are presented as medians . Classified variable data are presented as n (%). Statistical analyses were performed using R statistical software version 3.5.0 . The receiver operating characteristic (ROC) curves were plotted with the pROC package . A confiA total of 408 patients with confirmed COVID-19 were included in this study, with 303 patients in the Honghu cohort and 105 in the Nanchang cohort as the training set and test set, respectively. The demographics and baseline characteristics of the Honghu and Nanchang cohorts are listed in Using CT imaging and ResNet34, a disease severity prediction model based on MIL was developed in the training cohort. The cross-entropy loss was close to 0.03  and the To “uncover” the black box and make our model more intuitive, we displayed slices associated with severe and non-severe COVID-19 cases from six randomly selected patients (three severe and three non-severe). It can be observed that disease severity is positively correlated with the area and density of lesions in lungs. For the severe cases , multiplNext, the predictive accuracy of the model was evaluated via a confusion matrix. In the training dataset, the model correctly discriminated against 97.7% of patients in the non-severe group and 95.8% of patients in the severe group , with anThe clinical significance of the predictive model lies in its ability to identify patients in the early stages of the disease who were mildly ill on admission but progressed to severe disease later. To further estimate the practical significance of the MIL model, we performed a clinically important subgroup analysis. Patients with severe symptoms on admission were excluded from both the Honghu and the Nanchang cohorts, whereas those who presented non-severe symptoms on admission were retained.This model achieved AUCs as high as 0.955 (0.884–1.000)  and 0.92P < 0.001; Nanchang: P < 0.001, log-rank test)  . These rIn this retrospective study, we developed and validated a MIL-based predictive model using CT imaging of 408 patients to enable accurate identification of patients with severe COVID-19. Subgroup analyses in patients with non-severe COVID-19 on admission revealed that this model could accurately predict disease deterioration in the early stages. To our knowledge, this is the first study to demonstrate a novel application of MIL to establish a disease severity predictive model for COVID-19. This model has the potential to contribute to computer-aided diagnosis and guide clinical treatment by preemptively identifying patients who have a high risk of experiencing more severe symptoms.Many models have been developed to facilitate early identification of patients with a high risk of progression. The parameters used in these models are mainly clinical indicators and imaging findings, which are known for their role in the diagnosis and prognosis of COVID-19 as specified by the Chinese guideline for COVID-19 . PredictAt present, there are two main methods for predicting the risk of progression using chest CT. In one method, radiologists manually assign a score for each image to identify patients with potential severe disease, represented by the “CT severity score,” whereas the other method utilizes artificial intelligence to automatically calculate the risk of progression.The CT severity score in the first method  is a semConventional supervised learning in the artificial intelligence-assisted method requires labeling information and lesion annotation for each object to guide learning of the algorithm. On the one hand, labeling is slow and laborious, and radiologists working in a COVID-19 setting do not have sufficient time to label every slice of a CT image. On the other hand, it is difficult for radiologists to mark the lesion area very accurately; thus, both the normal and pathological tissues could inevitably exist in the marked area, resulting in a large amount of noise in the training set that consequently affects the accuracy of learning. However, the MIL method adopted in this study differs from a supervised machine learning technique. It requires only patient-level labels to inform the algorithm whether the patient would progress to a severe symptom. Labels on each chest CT image and annotation of the lesion area are not needed in this method, thereby saving radiologists precious time and improving the accuracy of the algorithm to some extent.In this section, the overall advantage of our model in clinical practice is highlighted. Our model exploited the superiority of CT imaging, making the prediction procedure fast, intuitive, and non-invasive. Compared with the CT severity score, our model enabled a relatively objective and accurate diagnosis of severe COVID-19, which increased reliability and improved quality control. Our model can maintain the same diagnostic criteria anywhere and ensure that the AUCs are as high as 0.987 and 0.892, with accuracies of 97.4 and 81.9% in the training and test sets, respectively. Compared with other supervised AI-assisted predictive models, our AI system eliminates the huge workload of annotating the lesion, allowing radiologists to save time and increasing their capacity to handle emergencies quickly and effectively, while maintaining high accuracy. Easing the workload of radiologists in terms of lesion annotations as well as quick and accurate prediction of the procedure will facilitate risk predictions in daily practice, which is extremely important for COVID-19 management owing to the severe limitations that occur with regard to healthcare resources.There are several limitations to this study. First, our sample size was relatively small, which would inevitably result in some bias. Additional patient data is required to validate our model. Moreover, a disadvantage of all deep learning methods is the lack of transparency and interpretability. More efforts to determine the correlation between clinical processes and the prediction results of our model should be made in the future.We employed MIL, a deep learning method, using quantitative CT data to accurately predict the disease severity of COVID-19. By utilizing an inexpensive and widely available test, our model can be used to identify patients at high risk of disease progression in the early phase of the disease, which has important practical implications for conducting early intervention, preventing disease progression, and reducing mortality. We recommend that confirmed COVID-19 patients should undergo CT screening as soon as they are admitted to the hospital, so that physicians can use our model to determine the risk of severe disease. If the result indicates a potential worsening of the condition of the patient, closer monitoring and early intervention should be considered before the disease severity increases. We hope that this model would be of some help to clinicians to better manage patients and contribute to combatting COVID-19.Reasonable data requests are welcome. A proposal and detailed illustration of the study aims will be needed to assess the reasonability of requests. After approval from participating hospitals and the corresponding authors, de-identified clinical data will be provided.The studies involving human participants were reviewed and approved by the Medical Ethics committee of Nanfang Hospital of Southern Medical University. The patients/participants provided their written informed consent to participate in this study.HZ had full access to all the data in the study and took responsibility for the integrity of the data and accuracy of the data analysis. LX, PL, FS, YZ, and CX contributed equally to this work. LX, LL, and WS contributed to concept and design. F-QC, Y-LH, W-FZ, MG, LL, and LX acquired, analyzed, and interpreted the data. LX, FS, YZ, and CX drafted the manuscript. PL, HBZ, and S-CM critically revised the manuscript for important intellectual content. LX, FS, CX, and CH contributed to the statistical analysis. HZ and LL obtained the funding. WS, MG, F-QC, Y-LH, and W-FZ contributed to administrative, technical, or material support. HZ, WS, and LL supervised the study. All the authors contributed to the article and approved the submitted version.FS, CX, MG, and WS were employed by the Digital China Health Technologies Corporation Limited . 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.