Archive 21/2/15

A group from the Center for Child Health and Development described a regenerative medicine approach to thin endometrium (endometrial stunting), which is one of causes of infertility.
https://stemcellres.biomedcentral.com/articles/10.1186/s13287-021-02188-x

There are various causes of infertility, but one of them is a thin endometrium (endometrial stunting). It is known that thin endometrium make ovoimplantation difficult, but little is known about the reason. Treatments for endometrial stunting include (1)estradiol replacement, mineral and vitamin administration, and from regenerative medicine approaches, (2) administration of platelet-rich plasma, (3) menstruation-derived stem cells, and (4) use of autologous transplantation of exogenously prepared endometrium cell sheets.

In a regenerative medicine approach, however, it would be very important how efficiently to culture the endometrium under xeno-free conditions. The endometrium is basically difficult to culture with feeder-free, and from the view point of xeno-free, the authors have developed a method of using autologous endometrial stromal cells as a feeder  instead of MEF. They also used special media (ESTEM-HE) in terms of accelerating endometrial cell growth. In the figure below, continental medium is DMEM and epithelium-specific medium is a special medium named ESTEM-HE.

A group from Grossman School of Medicine, New York has reported on the efficacy of Pfizer’s current new coronavirus (COVDI-19) vaccine for B.1.1.7 (UK lineage variant) and B.1.351 (South African lineage variant).
https://www.biorxiv.org/content/10.1101/2021.02.05.430003v1

This blog site also contains a similar research result from other research groups (see article on February 6, 2021).
The Group’s reporting has a similar trend. The current Pfizer vaccine is not so affected by UK variant, but it seems certain that its effectiveness is likely to be significantly lower for South African variant.

RT-PCR is the Gold Standard for the detection of the new coronavirus (SARS-CoV-2).
A group from Mahidol University, Nakhon Pathom, Thailand etc. has reported an electrochemical detection method that has the same sensitivity and specificity as RT-PCR and do not require expensive devices.
https://www.nature.com/articles/s41467-021-21121-7

In order to detect SARS-CoV-2 with high sensitivity, of course, a gene amplification process is required. They adopted Rolling Circle Amplification (RCA). The RCA method, like the PCR and LAMP methods, has the advantage that carryover contamination has a small impact on the next amplification process and can be amplified in an isothermal bath at around room temperature (25°C to 37°C). The RCA amplicon (Padlock probe) was designed as shown below and was consist of Target gene specific to the target you want to detect (N-gene, S-gene, etc. of SARS-CoV-2), Universal capture probe-binding region, and Generic reporter probe-binding region. With this Padlock probe, RCA results in long DNA amplicons containing hundreds of tandem repeats of the Padlock DNA complementary sequence. For this RCA products, hybridize probe-conjugated magnetic bead particle (CP-MNB) and Silica methylene blue reporter probe (SiMB-RP) to obtain the final product. Methylene blue or Acridine orange was used to as a redox regent. The current obtained by the electrochemical reaction increased with copy numbers as shown below. As a result, the detection limit sensitivity reached 1 copy/uL.

A group from University of Helsinki etc. has reported on the relationship between Vitamin C and COVID-19.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7848027/

In healthy people, a Vitamin C intake of about 0.1 g/day is enough to maintain the concentration of Vitamin C in the blood. However, when infected with COVID-19 and becomes severe, the concentration of Vitamin C in the blood decreases sharply, and it can be reduced to the same level as vitamin C deficiency and further to a level that is almost undetected.

Taking a large amount of Vitamin C (6-8 g/day) has the following effects:
Reduced ICU treatment duration by an average of 8%
Reduced fatality from 35% to 78%

Since there are no side effects of Vitamin C, this is also considered to be effective for the COVID-19 treatment.

A group from The First Affiliated Human of Xi’an Jiaotong University, Xi’an, China etc. has reported new biomarkers from bronchial alveoli lavage to identify adenocarcinoma, squamous carcinomas, and small cell lung cancer.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7840895/

Lectin microarrays were used for the marker screening, and bronchoalveolar lavage fluid was used as a sample. The goal was to identify lung adenocarcinomas (ADC), lung squamous carcinomas (SCC), and small cell lung cancer (SCLC) from benign pulmonary diseases (BPD). The goal was also to distinguish between early stage lung cancer (LC-ES) and advanced stage long cancer (LC-AS).

From the results of the lectin microarray, significant differences were found in 15  lectins, and a logistic regression analysis was performed by combining several lectins to identify the differences between various lung cancers.

As a result
To discriminate lung cancer from good lung disease, ECA, GSL-I, and RCA120 were used (cutoff value: 0.754, ALC: 0.961, sensitivity: 0.918, specificity: 0.939),
to discriminate ADC, DBA, STL, UEA-I, BPL were used (cutoff value: 0.569, ALC: 0.619, sensitivity: 0.706, specificity: 0.586),
to discriminate SCC, PNA was used (cutoff value: 0.578, ALC: 0.693, sensitivity: 0.800, specificity: 0.667),
to discriminate SCLC, STL, BS-I, PTL-II, SBA, PSA were used (cutoff value: 0.728, ALC: 0.718, sensitivity: 0.721, specificity: 0.684),
and also
to detect early cancer, MAL-II, LTL, GSL-I, RCA120, PTL-II, PWM were used, resulting in (cutoff value: 0.668, AOC: 0.856, sensitivity: 0.829, specificity: 0.810).

In order to distinguish various diseases, it is advantageous not to rely on one variable (one lectin), but to distinguish the characteristic profile for each disease using multiple variables (multiple lectins). In my view, I think that it will become a trend to adopt AI instead of using conventional statistical analysis to improve discrimination ability of the differences in disease profiles.